A Computational Introduction to Number Theory and Algebra (Version 2.2)
Victor Shoup
Version History 2.2: Mar. 6, 2008. More typos fixed, some improvements to exposition. 2.1: Jan. 21, 2008. Fixed some typos, changed to Times Roman font. 2.0: Dec. 18, 2007. Extensive organizational and notational changes, as well as corrections, some additional material, and many additional exercises. 1.0: Jan. 15, 2005. Corresponds to the first print edition, published by Cambridge University Press (2005).
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Copyright © 2007 by Victor Shoup
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Contents
page x xiv
Preface Preliminaries 1
Basic properties of the integers 1.1 Divisibility and primality 1.2 Ideals and greatest common divisors 1.3 Some consequences of unique factorization
1 1 5 10
2
Congruences 2.1 Equivalence relations 2.2 Definitions and basic properties of congruences 2.3 Solving linear congruences 2.4 The Chinese remainder theorem 2.5 Residue classes 2.6 Euler’s phi function 2.7 Euler’s theorem and Fermat’s little theorem 2.8 Quadratic residues 2.9 Summations over divisors
14 14 15 18 21 23 30 32 35 45
3
Computing with large integers 3.1 Asymptotic notation 3.2 Machine models and complexity theory 3.3 Basic integer arithmetic 3.4 Computing in Zn 3.5 Faster integer arithmetic ./ 3.6 Notes
49 49 52 54 64 68 70
4
Euclid’s algorithm 4.1 The basic Euclidean algorithm 4.2 The extended Euclidean algorithm 4.3 Computing modular inverses and Chinese remaindering
73 73 76 80
v
vi
Contents
4.4 4.5 4.6 4.7 4.8
Speeding up algorithms via modular computation An effective version of Fermat’s two squares theorem Rational reconstruction and applications The RSA cryptosystem Notes
82 85 88 98 101
5
The distribution of primes 5.1 Chebyshev’s theorem on the density of primes 5.2 Bertrand’s postulate 5.3 Mertens’ theorem 5.4 The sieve of Eratosthenes 5.5 The prime number theorem . . . and beyond 5.6 Notes
103 103 107 109 114 115 123
6
Abelian groups 6.1 Definitions, basic properties, and examples 6.2 Subgroups 6.3 Cosets and quotient groups 6.4 Group homomorphisms and isomorphisms 6.5 Cyclic groups 6.6 The structure of finite abelian groups ./
125 125 131 136 141 152 162
7
Rings 7.1 Definitions, basic properties, and examples 7.2 Polynomial rings 7.3 Ideals and quotient rings 7.4 Ring homomorphisms and isomorphisms 7.5 The structure of Zn
165 165 175 184 190 202
8
Finite and discrete probability distributions 8.1 Basic definitions 8.2 Conditional probability and independence 8.3 Random variables 8.4 Expectation and variance 8.5 Some useful bounds 8.6 Balls and bins 8.7 Hash functions 8.8 Statistical distance 8.9 Measures of randomness and the leftover hash lemma ./ 8.10 Discrete probability distributions 8.11 Notes
206 206 212 220 233 240 244 251 259 264 269 274
Contents
vii
9
Probabilistic algorithms 9.1 Basic definitions 9.2 Generating a random number from a given interval 9.3 The generate and test paradigm 9.4 Generating a random prime 9.5 Generating a random non-increasing sequence 9.6 Generating a random factored number 9.7 Some complexity theory 9.8 Notes
275 276 283 285 290 293 296 300 302
10
Probabilistic primality testing 10.1 Trial division 10.2 The Miller–Rabin test 10.3 Generating random primes using the Miller–Rabin test 10.4 Factoring and computing Euler’s phi function 10.5 Notes
304 304 305 309 318 322
11
Finding generators and discrete logarithms in Zp 11.1 Finding a generator for Zp 11.2 Computing discrete logarithms in Zp 11.3 The Diffie–Hellman key establishment protocol 11.4 Notes
325 325 327 333 338
12
Quadratic reciprocity and computing modular square roots 12.1 The Legendre symbol 12.2 The Jacobi symbol 12.3 Computing the Jacobi symbol 12.4 Testing quadratic residuosity 12.5 Computing modular square roots 12.6 The quadratic residuosity assumption 12.7 Notes
340 340 344 346 347 348 354 355
13
Modules and vector spaces 13.1 Definitions, basic properties, and examples 13.2 Submodules and quotient modules 13.3 Module homomorphisms and isomorphisms 13.4 Linear independence and bases 13.5 Vector spaces and dimension
357 357 359 361 366 369
14
Matrices 14.1 Basic definitions and properties 14.2 Matrices and linear maps 14.3 The inverse of a matrix
376 376 380 385
viii
Contents
14.4 Gaussian elimination 14.5 Applications of Gaussian elimination 14.6 Notes
387 391 397
15
Subexponential-time discrete logarithms and factoring 15.1 Smooth numbers 15.2 An algorithm for discrete logarithms 15.3 An algorithm for factoring integers 15.4 Practical improvements 15.5 Notes
398 398 399 406 413 418
16
More rings 16.1 Algebras 16.2 The field of fractions of an integral domain 16.3 Unique factorization of polynomials 16.4 Polynomial congruences 16.5 Minimal polynomials 16.6 General properties of extension fields 16.7 Formal derivatives 16.8 Formal power series and Laurent series 16.9 Unique factorization domains ./ 16.10 Notes
420 420 425 428 433 436 438 442 444 448 462
17
Polynomial arithmetic and applications 17.1 Basic arithmetic 17.2 Computing minimal polynomials in F ŒX=.f / (I) 17.3 Euclid’s algorithm 17.4 Computing modular inverses and Chinese remaindering 17.5 Rational function reconstruction and applications 17.6 Faster polynomial arithmetic ./ 17.7 Notes
463 463 466 467 470 472 476 482
18
Linearly generated sequences and applications 18.1 Basic definitions and properties 18.2 Computing minimal polynomials: a special case 18.3 Computing minimal polynomials: a more general case 18.4 Solving sparse linear systems 18.5 Computing minimal polynomials in F ŒX=.f / (II) 18.6 The algebra of linear transformations ./ 18.7 Notes
484 484 488 490 495 498 499 506
19
Finite fields 19.1 Preliminaries
507 507
Contents
20
21
ix
19.2 The existence of finite fields 19.3 The subfield structure and uniqueness of finite fields 19.4 Conjugates, norms and traces
509 513 515
Algorithms for finite fields 20.1 Generating and constructing irreducible polynomials 20.2 Computing minimal polynomials in F ŒX=.f / (III) 20.3 Factoring polynomials: square-free decomposition 20.4 Factoring polynomials: the Cantor–Zassenhaus algorithm 20.5 Factoring polynomials: Berlekamp’s algorithm 20.6 Deterministic factorization algorithms ./ 20.7 Notes
521 521 524 525 529 537 543 545
Deterministic primality testing 21.1 The basic idea 21.2 The algorithm and its analysis 21.3 Notes Appendix: Some useful facts Bibliography Index of notation Index
547 547 548 558 559 564 570 572
Preface
Number theory and algebra play an increasingly significant role in computing and communications, as evidenced by the striking applications of these subjects to such fields as cryptography and coding theory. My goal in writing this book was to provide an introduction to number theory and algebra, with an emphasis on algorithms and applications, that would be accessible to a broad audience. In particular, I wanted to write a book that would be accessible to typical students in computer science or mathematics who have a some amount of general mathematical experience, but without presuming too much specific mathematical knowledge. Prerequisites. The mathematical prerequisites are minimal: no particular mathematical concepts beyond what is taught in a typical undergraduate calculus sequence are assumed. The computer science prerequisites are also quite minimal: it is assumed that the reader is proficient in programming, and has had some exposure to the analysis of algorithms, essentially at the level of an undergraduate course on algorithms and data structures. Even though it is mathematically quite self contained, the text does presuppose that the reader is comfortable with mathematical formalism and has some experience in reading and writing mathematical proofs. Readers may have gained such experience in computer science courses such as algorithms, automata or complexity theory, or some type of “discrete mathematics for computer science students” course. They also may have gained such experience in undergraduate mathematics courses, such as abstract or linear algebra — these courses overlap with some of the material presented here, but even if the reader already has had some exposure to this material, it nevertheless may be convenient to have all of the relevant material easily accessible in one place, and moreover, the emphasis and perspective here will no doubt be different than in a typical mathematics course on these subjects. Structure of the text. All of the mathematics required beyond basic calculus is dex
Preface
xi
veloped “from scratch.” Moreover, the book generally alternates between “theory” and “applications”: one or two chapters on a particular set of purely mathematical concepts are followed by one or two chapters on algorithms and applications — the mathematics provides the theoretical underpinnings for the applications, while the applications both motivate and illustrate the mathematics. Of course, this dichotomy between theory and applications is not perfectly maintained: the chapters that focus mainly on applications include the development of some of the mathematics that is specific to a particular application, and very occasionally, some of the chapters that focus mainly on mathematics include a discussion of related algorithmic ideas as well. In developing the mathematics needed to discuss certain applications, I tried to strike a reasonable balance between, on the one hand, presenting the absolute minimum required to understand and rigorously analyze the applications, and on the other hand, presenting a full-blown development of the relevant mathematics. In striking this balance, I wanted to be fairly economical and concise, while at the same time, I wanted to develop enough of the theory so as to present a fairly wellrounded account, giving the reader more of a feeling for the mathematical “big picture.” The mathematical material covered includes the basics of number theory (including unique factorization, congruences, the distribution of primes, and quadratic reciprocity) and abstract algebra (including groups, rings, fields, and vector spaces). It also includes an introduction to discrete probability theory — this material is needed to properly treat the topics of probabilistic algorithms and cryptographic applications. The treatment of all these topics is more or less standard, except that the text only deals with commutative structures (i.e., abelian groups and commutative rings with unity) — this is all that is really needed for the purposes of this text, and the theory of these structures is much simpler and more transparent than that of more general, non-commutative structures. The choice of topics covered in this book was motivated primarily by their applicability to computing and communications, especially to the specific areas of cryptography and coding theory. For example, the book may be useful for reference or self-study by readers who want to learn about cryptography. The book could also be used as a textbook in a graduate or upper-division undergraduate course on (computational) number theory and algebra, perhaps geared towards computer science students. Since this is an introductory textbook, and not an encyclopedic reference for specialists, some topics simply could not be covered. One such topic whose exclusion will undoubtedly be lamented by some is the theory of lattices, along with algorithms for and applications of lattice basis reduction. Another such topic is that of fast algorithms for integer and polynomial arithmetic — although some of
xii
Preface
the basic ideas of this topic are developed in the exercises, the main body of the text deals only with classical, quadratic-time algorithms for integer and polynomial arithmetic. As an introductory text, some topics just had to go; moreover, there are more advanced texts that cover these topics perfectly well, and these texts should be readily accessible to students who have mastered the material in this book. Note that while continued fractions are not discussed, the closely related problem of “rational reconstruction” is covered, along with a number of interesting applications (which could also be solved using continued fractions). Using the text. Here are a few guidelines on using the text. There are a few sections that are marked with a “./,” indicating that the material covered in that section is a bit technical, and is not needed elsewhere. There are many examples in the text. These form an integral part of the text, and should not be skipped. There are a number of exercises in the text that serve to reinforce, as well as to develop important applications and generalizations of, the material presented in the text. Some exercises are underlined. These specially marked exercises develop important (but usually simple) facts, and should be viewed as an integral part of the text. It is highly recommended that the reader work these exercises, or at the very least, read and understand their statements. In solving exercises, the reader is free to use any previously stated results in the text, including those in previous exercises. However, except where otherwise noted, any result in a section marked with a “./,” or in §5.5, need not and should not be used outside the section in which it appears. There is a very brief “Preliminaries” chapter, which fixes a bit of notation and recalls a few standard facts. This should be skimmed over by the reader. There is an appendix that contains a few useful facts; where such a fact is used in the text, there is a reference such as “see §An,” which refers to the item labeled “An” in the appendix. Feedback. I welcome comments on the book (suggestions for improvement, error reports, etc.) from readers. Please send your comments to [email protected]. There is also a web site where further material and information relating to the book (including a list of errata and the latest electronic version of the book) may be found: www.shoup.net/ntb.
Preface
xiii
Acknowledgments. I would like to thank a number of people who volunteered their time and energy in reviewing parts of the book at different stages: Siddhartha Annapureddy, John Black, Carl Bosley, Joshua Brody, Jan Camenisch, Ronald Cramer, Alex Dent, Nelly Fazio, Mark Giesbrecht, Stuart Haber, Gene Itkis, Alfred Menezes, Antonio Nicolosi, Roberto Oliveira, Louis Salvail, and George Stephanides. I am also grateful to the National Science Foundation for their support provided under grant CCR-0310297. Thanks to David Tranah and his colleagues at Cambridge University Press for their progressive attitudes regarding intellectual property and open access. Victor Shoup
Preliminaries
We establish here some terminology, notation, and simple facts that will be used throughout the text. Logarithms and exponentials We write log x for the natural logarithm of x, and logb x for the logarithm of x to the base b. We write e x for the usual exponential function, where e 2:71828 is the base of the natural logarithm. We may also write expŒx instead of e x . Numbers We use standard notation for various sets of numbers: Z WD the set of integers D f: : : ; 2; 1; 0; 1; 2; : : :g; Q WD the set of rational numbers D fa=b W a; b 2 Z; b ¤ 0g; R WD the set of real numbers; C WD the set of complex numbers: We sometimes use the symbols 1 and 1 in simple arithmetic expressions involving real numbers. The interpretation given to such expressions should be obvious: for example, for every x 2 R, we have 1 < x < 1, x C 1 D 1, x 1 D 1, 1 C 1 D 1, and . 1/ C . 1/ D 1. Expressions such as x .˙1/ also make sense, provided x ¤ 0. However, the expressions 1 1 and 0 1 have no sensible interpretation. We use standard notation for specifying intervals of real numbers: for a; b 2 R
xiv
xv
Preliminaries
with a b, Œa; b WD fx 2 R W a x bg;
.a; b/ WD fa 2 R W a < x < bg;
Œa; b/ WD fx 2 R W a x < bg;
.a; b WD fa 2 R W a < x bg:
As usual, this notation is extended to allow a D and b D 1 for intervals Œa; b/ and .a; b/.
1 for intervals .a; b and .a; b/,
Sets and families We use standard set-theoretic notation: ; denotes the empty set; x 2 A means that x is an element, or member, of the set A; for two sets A; B, A B means that A is a subset of B (with A possibly equal to B), and A ¨ B means that A is a proper subset of B (i.e., A B but A ¤ B). Further, A [ B denotes the union of A and B, A \ B the intersection of A and B, and A n B the set of all elements of A that are not in B. If A is a set with a finite number of elements, then we write jAj for its size, or cardinality. We use standard notation for specifying the elements of a set. For example, the set of all even integers could be specified as fz 2 Z W z=2 2 Zg or as f2z W z 2 Zg. We write S1 Sn for the Cartesian product of sets S1 ; : : : ; Sn , that is, the set of all n-tuples .a1 ; : : : ; an /, where ai 2 Si for i D 1; : : : ; n. We write S n for the Cartesian product of n copies of a set S, and for x 2 S , we write x n for the element of S n consisting of n copies of x. (This notation is a bit non-standard, but we reserve the more standard notation S n for other purposes, so as to avoid ambiguity.) A family is a collection of objects, indexed by some set I , called an index set. If for each i 2 I we have an associated object xi , the family of all such objects is denoted by fxi gi 2I . Unlike a set, a family may contain duplicates; that is, we may have xi D xj for some pair of indices i; j with i ¤ j . If the index set I has some natural order, then we may view the family as being ordered in the same way. As a special case, a family indexed by a subset of Z of the form fm; : : : ; ng or fm; m C 1; : : :g is a sequence, which we may write as fxi gniDm or fxi g1 i Dm . Note that while fxi gi 2I denotes a family, fxi W i 2 I g denotes the set whose members are the (distinct) xi ’s. On occasion, if the choice of index set is not important, we may simply define a family by listing or describing its members, without explicitly describing an index set; for example, “the family of objects a; b; c,” means the family fxi g3iD1 , where x1 WD a, x2 WD b, and x3 WD c. Unions and intersections may be generalized to arbitrary families of sets. For a family fSi gi 2I of sets, the union is [ Si WD fx W x 2 Si for some i 2 I g; i 2I
xvi
Preliminaries
and for I ¤ ;, the intersection is \ Si WD fx W x 2 Si for all i 2 I g: i 2I
Note that if I D ;, the union is by definition ;, but the intersection is, in general, not well defined; however, in certain application, one might define it by a special convention; for example, if all sets under consideration are subsets of some “ambient space,” ˝, then the empty intersection is usually taken to be ˝. Two sets A and B are called disjoint if A \ B D ;. A family fSi gi 2I of sets is called pairwise disjoint if Si \ Sj D ; for all i; j 2 I with i ¤ j . A pairwise disjoint family of non-empty sets whose union is S is called a partition of S ; equivalently, fSi gi 2I is a partition of a set S if each Si is a non-empty subset of S , and each element of S belongs to exactly one Si . Functions We write f W A ! B to indicate that f is a function (also called a map) from a set A to a set B. If A0 A, then f .A0 / WD ff .a/ W a 2 A0 g is the image of A0 under f , and f .A/ is simply referred to as the image of f ; if B 0 B, then f 1 .B 0 / WD fa 2 A W f .a/ 2 B 0 g is the pre-image of B 0 under f . A function f W A ! B is called one-to-one or injective if f .a/ D f .b/ implies a D b. The function f is called onto or surjective if f .A/ D B. The function f is called bijective if it is both injective and surjective; in this case, f is called a bijection, or a one-to-one correspondence. If f is bijective, then we may define the inverse function f 1 W B ! A, where for b 2 B, f 1 .b/ is defined to be the unique a 2 A such that f .a/ D b; in this case, f 1 is also a bijection, and .f 1 / 1 D f . If A0 A, then the inclusion map from A0 to A is the function i W A0 ! A given by i.a/ WD a for a 2 A0 ; when A0 D A, this is called the identity map on A. If A0 A, f 0 W A0 ! B, f W A ! B, and f 0 .a/ D f .a/ for all a 2 A0 , then we say that f 0 is the restriction of f to A0 , and that f is an extension of f 0 to A. If f W A ! B and g W B ! C are functions, their composition is the function g B f W A ! C given by .g B f /.a/ WD g.f .a// for a 2 A. If f W A ! B is a bijection, then f 1 B f is the identity map on A, and f B f 1 is the identity map on B. Conversely, if f W A ! B and g W B ! A are functions such that g B f is the identity map on A and f B g is the identity map on B, then f and g are bijections, each being the inverse of the other. If f W A ! B and g W B ! C are bijections, then so is g B f , and .g B f / 1 D f 1 B g 1 . Function composition is associative; that is, for all functions f W A ! B, g W B ! C , and h W C ! D, we have .h B g/ B f D h B .g B f /. Thus, we
Preliminaries
xvii
can simply write h B g B f without any ambiguity. More generally, if we have functions fi W Ai ! AiC1 for i D 1; : : : ; n, where n 2, then we may write their composition as fn B B f1 without any ambiguity. If each fi is a bijection, then so is fn B B f1 , its inverse being f1 1 B B fn 1 . As a special case of this, if Ai D A and fi D f for i D 1; : : : ; n, then we may write fn B B f1 as f n . It is understood that f 1 D f , and that f 0 is the identity map on A. If f is a bijection, then so is f n for every non-negative integer n, the inverse function of f n being .f 1 /n , which one may simply write as f n . If f W I ! S is a function, then we may view f as the family fxi gi 2I , where xi WD f .i/. Conversely, a family fxi gi 2I , where all of the xi ’s belong to some set S, may be viewed as the function f W I ! S given by f .i / WD xi for i 2 I . Really, functions and families are the same thing, the difference being just one of notation and emphasis. Binary operations A binary operation ? on a set S is a function from S S to S, where the value of the function at .a; b/ 2 S S is denoted a ? b. A binary operation ? on S is called associative if for all a; b; c 2 S , we have .a ? b/ ? c D a ? .b ? c/. In this case, we can simply write a ? b ? c without any ambiguity. More generally, for a1 ; : : : ; an 2 S, where n 2, we can write a1 ? ? an without any ambiguity. A binary operation ? on S is called commutative if for all a; b 2 S , we have a ? b D b ? a. If the binary operation ? is both associative and commutative, then not only is the expression a1 ? ? an unambiguous, but its value remains unchanged even if we re-order the ai ’s. If ? is a binary operation on S , and S 0 S , then S 0 is called closed under ? if a ? b 2 S 0 for all a; b 2 S 0 .
1 Basic properties of the integers
This chapter discusses some of the basic properties of the integers, including the notions of divisibility and primality, unique factorization into primes, greatest common divisors, and least common multiples. 1.1 Divisibility and primality A central concept in number theory is divisibility. Consider the integers Z D f: : : ; 2; 1; 0; 1; 2; : : :g. For a; b 2 Z, we say that a divides b if az D b for some z 2 Z. If a divides b, we write a j b, and we may say that a is a divisor of b, or that b is a multiple of a, or that b is divisible by a. If a does not divide b, then we write a − b. We first state some simple facts about divisibility: Theorem 1.1. For all a; b; c 2 Z, we have (i) a j a, 1 j b, and a j 0; (ii) 0 j b if and only if b D 0; (iii) a j b if and only if a j b if and only if a j (iv) a j b and a j c implies a j .b C c/; (v) a j b and b j c implies a j c.
b;
Proof. These properties can be easily derived from the definition of divisibility, using elementary algebraic properties of the integers. For example, a j a because we can write a 1 D a; 1 j b because we can write 1 b D b; a j 0 because we can write a 0 D 0. We leave it as an easy exercise for the reader to verify the remaining properties. We make a simple observation: if a j b and b ¤ 0, then 1 jaj jbj. Indeed, if az D b ¤ 0 for some integer z, then a ¤ 0 and z ¤ 0; it follows that jaj 1, jzj 1, and so jaj jajjzj D jbj. 1
2
Basic properties of the integers
Theorem 1.2. For all a; b 2 Z, we have a j b and b j a if and only if a D ˙b. In particular, for every a 2 Z, we have a j 1 if and only if a D ˙1. Proof. Clearly, if a D ˙b, then a j b and b j a. So let us assume that a j b and b j a, and prove that a D ˙b. If either of a or b are zero, then the other must be zero as well. So assume that neither is zero. By the above observation, a j b implies jaj jbj, and b j a implies jbj jaj; thus, jaj D jbj, and so a D ˙b. That proves the first statement. The second statement follows from the first by setting b WD 1, and noting that 1 j a. The product of any two non-zero integers is again non-zero. This implies the usual cancellation law: if a, b, and c are integers such that a ¤ 0 and ab D ac, then we must have b D c; indeed, ab D ac implies a.b c/ D 0, and so a ¤ 0 implies b c D 0, and hence b D c. Primes and composites. Let n be a positive integer. Trivially, 1 and n divide n. If n > 1 and no other positive integers besides 1 and n divide n, then we say n is prime. If n > 1 but n is not prime, then we say that n is composite. The number 1 is not considered to be either prime or composite. Evidently, n is composite if and only if n D ab for some integers a; b with 1 < a < n and 1 < b < n. The first few primes are 2; 3; 5; 7; 11; 13; 17; : : : : While it is possible to extend the definition of prime and composite to negative integers, we shall not do so in this text: whenever we speak of a prime or composite number, we mean a positive integer. A basic fact is that every non-zero integer can be expressed as a signed product of primes in an essentially unique way. More precisely: Theorem 1.3 (Fundamental theorem of arithmetic). Every non-zero integer n can be expressed as n D ˙p1e1 prer ; where p1 ; : : : ; pr are distinct primes and e1 ; : : : ; er are positive integers. Moreover, this expression is unique, up to a reordering of the primes. Note that if n D ˙1 in the above theorem, then r D 0, and the product of zero terms is interpreted (as usual) as 1. The theorem intuitively says that the primes act as the “building blocks” out of which all non-zero integers can be formed by multiplication (and negation). The reader may be so familiar with this fact that he may feel it is somehow “self evident,” requiring no proof; however, this feeling is simply a delusion, and most
3
1.1 Divisibility and primality
of the rest of this section and the next are devoted to developing a proof of this theorem. We shall give a quite leisurely proof, introducing a number of other very important tools and concepts along the way that will be useful later. To prove Theorem 1.3, we may clearly assume that n is positive, since otherwise, we may multiply n by 1 and reduce to the case where n is positive. The proof of the existence part of Theorem 1.3 is easy. This amounts to showing that every positive integer n can be expressed as a product (possibly empty) of primes. We may prove this by induction on n. If n D 1, the statement is true, as n is the product of zero primes. Now let n > 1, and assume that every positive integer smaller than n can be expressed as a product of primes. If n is a prime, then the statement is true, as n is the product of one prime; otherwise, n is composite, and so there exist a; b 2 Z with 1 < a < n, 1 < b < n, and n D ab; by the induction hypothesis, both a and b can be expressed as a product of primes, and so the same holds for n. The uniqueness part of Theorem 1.3 is the hard part. An essential ingredient in this proof is the following: Theorem 1.4 (Division with remainder property). Let a; b 2 Z with b > 0. Then there exist unique q; r 2 Z such that a D bq C r and 0 r < b. Proof. Consider the set S of non-negative integers of the form a bt with t 2 Z. This set is clearly non-empty; indeed, if a 0, set t WD 0, and if a < 0, set t WD a. Since every non-empty set of non-negative integers contains a minimum, we define r to be the smallest element of S. By definition, r is of the form r D a bq for some q 2 Z, and r 0. Also, we must have r < b, since otherwise, r b would be an element of S smaller than r, contradicting the minimality of r; indeed, if r b, then we would have 0 r b D a b.q C 1/. That proves the existence of r and q. For uniqueness, suppose that a D bq C r and a D bq 0 C r 0 , where 0 r < b and 0 r 0 < b. Then subtracting these two equations and rearranging terms, we obtain r0
r D b.q
q 0 /:
Thus, r 0 r is a multiple of b; however, 0 r < b and 0 r 0 < b implies jr 0 rj < b; therefore, the only possibility is r 0 r D 0. Moreover, 0 D b.q q 0 / and b ¤ 0 implies q q 0 D 0. Theorem 1.4 can be visualized as follows:
0
r
b
2b
3b
a
4b
4
Basic properties of the integers
Starting with a, we subtract (or add, if a is negative) the value b until we end up with a number in the interval Œ0; b/. Floors and ceilings. Let us briefly recall the usual floor and ceiling functions, denoted bc and de, respectively. These are functions from R (the real numbers) to Z. For x 2 R, bxc is the greatest integer m x; equivalently, bxc is the unique integer m such that m x < m C 1, or put another way, such that x D m C for some 2 Œ0; 1/. Also, dxe is the smallest integer m x; equivalently, dxe is the unique integer m such that m 1 < x m, or put another way, such that x D m for some 2 Œ0; 1/. The mod operator. Now let a; b 2 Z with b > 0. If q and r are the unique integers from Theorem 1.4 that satisfy a D bq C r and 0 r < b, we define a mod b WD rI that is, a mod b denotes the remainder in dividing a by b. It is clear that b j a if and only if a mod b D 0. Dividing both sides of the equation a D bq C r by b, we obtain a=b D q C r=b. Since q 2 Z and r=b 2 Œ0; 1/, we see that q D ba=bc. Thus, .a mod b/ D a
bba=bc:
One can use this equation to extend the definition of a mod b to all integers a and b, with b ¤ 0; that is, for b < 0, we simply define a mod b to be a bba=bc. Theorem 1.4 may be generalized so that when dividing an integer a by a positive integer b, the remainder is placed in an interval other than Œ0; b/. Let x be any real number, and consider the interval Œx; x C b/. As the reader may easily verify, this interval contains precisely b integers, namely, dxe; : : : ; dxe C b 1. Applying Theorem 1.4 with a dxe in place of a, we obtain: Theorem 1.5. Let a; b 2 Z with b > 0, and let x 2 R. Then there exist unique q; r 2 Z such that a D bq C r and r 2 Œx; x C b/. E XERCISE 1.1. Let a; b; d 2 Z with d ¤ 0. Show that a j b if and only if da j db. E XERCISE 1.2. Let n be a composite integer. Show that there exists a prime p dividing n, with p n1=2 : E XERCISE 1.3. Let m be a positive integer. Show that for every real number x 1, the number of multiples of m in the interval Œ1; x is bx=mc; in particular, for every integer n 1, the number of multiples of m among 1; : : : ; n is bn=mc.
1.2 Ideals and greatest common divisors
5
E XERCISE 1.4. Let x 2 R. Show that 2bxc b2xc 2bxc C 1. E XERCISE 1.5. Let x 2 R and n 2 Z with n > 0. Show that bbxc=nc D bx=nc; in particular, bba=bc=cc D ba=bcc for all positive integers a; b; c. E XERCISE 1.6. Let a; b 2 Z with b < 0. Show that .a mod b/ 2 .b; 0. E XERCISE 1.7. Show that Theorem 1.5 also holds for intervals of the form .x; x C b. Does it hold in general for the intervals Œx; x C b or .x; x C b/? 1.2 Ideals and greatest common divisors To carry on with the proof of Theorem 1.3, we introduce the notion of an ideal of Z, which is a non-empty set of integers that is closed under addition, and under multiplication by an arbitrary integer. That is, a non-empty set I Z is an ideal if and only if for all a; b 2 I and all z 2 Z, we have a C b 2 I and az 2 I: It is easy to see that every ideal I contains 0: since a 2 I for some integer a, we have 0 D a 0 2 I . Also, note that if an ideal I contains an integer a, it also contains a, since a D a . 1/ 2 I . Thus, if an ideal contains a and b, it also contains a b. It is clear that f0g and Z are ideals. Moreover, an ideal I is equal to Z if and only if 1 2 I ; to see this, note that 1 2 I implies that for every z 2 Z, we have z D 1 z 2 I , and hence I D Z; conversely, if I D Z, then in particular, 1 2 I. For a 2 Z, define aZ WD faz W z 2 Zg; that is, aZ is the set of all multiples of a. If a D 0, then clearly aZ D f0g; otherwise, aZ consists of the distinct integers : : : ; 3a; 2a; a; 0; a; 2a; 3a; : : : : It is easy to see that aZ is an ideal: for all az; az 0 2 aZ and z 00 2 Z, we have az C az 0 D a.z C z 0 / 2 aZ and .az/z 00 D a.zz 00 / 2 aZ. The ideal aZ is called the ideal generated by a, and an ideal of the form aZ for some a 2 Z is called a principal ideal. Observe that for all a; b 2 Z, we have b 2 aZ if and only if a j b. Also observe that for every ideal I , we have b 2 I if and only if bZ I . Both of these observations are simple consequences of the definitions, as the reader may verify. Combining these two observations, we see that bZ aZ if and only if a j b. If I1 and I2 are ideals, then it is not hard to see that the set I1 C I2 WD fa1 C a2 W a1 2 I1 ; a2 2 I2 g is also an ideal. Indeed, suppose a1 Ca2 2 I1 CI2 and b1 Cb2 2 I1 C I2 . Then we have .a1 C a2 / C .b1 C b2 / D .a1 C b1 / C .a2 C b2 / 2 I1 C I2 , and for every z 2 Z, we have .a1 C a2 /z D a1 z C a2 z 2 I1 C I2 .
6
Basic properties of the integers
Example 1.1. Consider the principal ideal 3Z. This consists of all multiples of 3; that is, 3Z D f: : : ; 9; 6; 3; 0; 3; 6; 9; : : :g. Example 1.2. Consider the ideal 3Z C 5Z. This ideal contains 3 2 C 5 . 1/ D 1. Since it contains 1, it contains all integers; that is, 3Z C 5Z D Z. Example 1.3. Consider the ideal 4Z C 6Z. This ideal contains 4 . 1/ C 6 1 D 2, and therefore, it contains all even integers. It does not contain any odd integers, since the sum of two even integers is again even. Thus, 4Z C 6Z D 2Z. In the previous two examples, we defined an ideal that turned out upon closer inspection to be a principal ideal. This was no accident: the following theorem says that all ideals of Z are principal. Theorem 1.6. Let I be an ideal of Z. Then there exists a unique non-negative integer d such that I D d Z. Proof. We first prove the existence part of the theorem. If I D f0g, then d D 0 does the job, so let us assume that I ¤ f0g. Since I contains non-zero integers, it must contain positive integers, since if a 2 I then so is a. Let d be the smallest positive integer in I . We want to show that I D d Z. We first show that I d Z. To this end, let a be any element in I . It suffices to show that d j a. Using the division with remainder property, write a D dq C r, where 0 r < d . Then by the closure properties of ideals, one sees that r D a dq is also an element of I , and by the minimality of the choice of d , we must have r D 0. Thus, d j a. We have shown that I d Z. The fact that d Z I follows from the fact that d 2 I . Thus, I D d Z. That proves the existence part of the theorem. As for uniqueness, note that if d Z D eZ for some non-negative integer e, then d j e and e j d , from which it follows by Theorem 1.2 that d D ˙e; since d and e are non-negative, we must have d D e. Greatest common divisors. For a; b 2 Z, we call d 2 Z a common divisor of a and b if d j a and d j b; moreover, we call such a d a greatest common divisor of a and b if d is non-negative and all other common divisors of a and b divide d . Theorem 1.7. For all a; b 2 Z, there exists a unique greatest common divisor d of a and b, and moreover, aZ C bZ D d Z. Proof. We apply the previous theorem to the ideal I WD aZ C bZ. Let d 2 Z with I D d Z, as in that theorem. We wish to show that d is a greatest common divisor of a and b. Note that a; b; d 2 I and d is non-negative.
7
1.2 Ideals and greatest common divisors
Since a 2 I D d Z, we see that d j a; similarly, d j b. So we see that d is a common divisor of a and b. Since d 2 I D aZ C bZ, there exist s; t 2 Z such that as C bt D d . Now suppose a D a0 d 0 and b D b 0 d 0 for some a0 ; b 0 ; d 0 2 Z. Then the equation as C bt D d implies that d 0 .a0 s C b 0 t / D d , which says that d 0 j d . Thus, any common divisor d 0 of a and b divides d . That proves that d is a greatest common divisor of a and b. As for uniqueness, note that if e is a greatest common divisor of a and b, then d j e and e j d , and hence d D ˙e; since both d and e are non-negative by definition, we have d D e. For a; b 2 Z, we write gcd.a; b/ for the greatest common divisor of a and b. We say that a; b 2 Z are relatively prime if gcd.a; b/ D 1, which is the same as saying that the only common divisors of a and b are ˙1. The following is essentially just a restatement of Theorem 1.7, but we state it here for emphasis: Theorem 1.8. Let a; b; r 2 Z and let d WD gcd.a; b/. Then there exist s; t 2 Z such that as C bt D r if and only if d j r. In particular, a and b are relatively prime if and only if there exist integers s and t such that as C bt D 1. Proof. We have as C bt D r for some s; t 2 Z ” r 2 aZ C bZ ” r 2 d Z (by Theorem 1.7) ” d j r: That proves the first statement. The second statement follows from the first, setting r WD 1. Note that as we have defined it, gcd.0; 0/ D 0. Also note that when at least one of a or b are non-zero, gcd.a; b/ may be characterized as the largest positive integer that divides both a and b, and as the smallest positive integer that can be expressed as as C bt for integers s and t. Theorem 1.9. Let a; b; c 2 Z such that c j ab and gcd.a; c/ D 1. Then c j b. Proof. Suppose that c j ab and gcd.a; c/ D 1. Then since gcd.a; c/ D 1, by Theorem 1.8 we have as C ct D 1 for some s; t 2 Z. Multiplying this equation by b, we obtain abs C cbt D b:
(1.1)
8
Basic properties of the integers
Since c divides ab by hypothesis, and since c clearly divides cbt, it follows that c divides the left-hand side of (1.1), and hence that c divides b. Suppose that p is a prime and a is any integer. As the only divisors of p are ˙1 and ˙p, we have p j a H) gcd.a; p/ D p; and p − a H) gcd.a; p/ D 1: Combining this observation with the previous theorem, we have: Theorem 1.10. Let p be prime, and let a; b 2 Z. Then p j ab implies that p j a or p j b. Proof. Assume that p j ab. If p j a, we are done, so assume that p − a. By the above observation, gcd.a; p/ D 1, and so by Theorem 1.9, we have p j b. An obvious corollary to Theorem 1.10 is that if a1 ; : : : ; ak are integers, and if p is a prime that divides the product a1 ak , then p j ai for some i D 1; : : : ; k. This is easily proved by induction on k. For k D 1, the statement is trivially true. Now let k > 1, and assume that statement holds for k 1. Then by Theorem 1.10, either p j a1 or p j a2 ak ; if p j a1 , we are done; otherwise, by induction, p divides one of a2 ; : : : ; ak . Finishing the proof of Theorem 1.3. We are now in a position to prove the uniqueness part of Theorem 1.3, which we can state as follows: if p1 ; : : : ; pr are primes (not necessarily distinct), and q1 ; : : : ; qs are primes (also not necessarily distinct), such that p1 pr D q1 qs ;
(1.2)
then .p1 ; : : : ; pr / is just a reordering of .q1 ; : : : ; qs /. We may prove this by induction on r. If r D 0, we must have s D 0 and we are done. Now suppose r > 0, and that the statement holds for r 1. Since r > 0, we clearly must have s > 0. Also, as p1 obviously divides the left-hand side of (1.2), it must also divide the right-hand side of (1.2); that is, p1 j q1 qs . It follows from (the corollary to) Theorem 1.10 that p1 j qj for some j D 1; : : : ; s, and moreover, since qj is prime, we must have p1 D qj . Thus, we may cancel p1 from the left-hand side of (1.2) and qj from the right-hand side of (1.2), and the statement now follows from the induction hypothesis. That proves the uniqueness part of Theorem 1.3. E XERCISE 1.8. Let I be a non-empty set of integers that is closed under addition, that is, a C b 2 I for all a; b 2 I . Show that I is an ideal if and only if a 2 I for all a 2 I .
1.2 Ideals and greatest common divisors
9
E XERCISE 1.9. Show that for all integers a; b; c, we have (a) gcd.a; b/ D gcd.b; a/, (b) gcd.a; b/ D jaj ” a j b, (c) gcd.a; 0/ D gcd.a; a/ D jaj and gcd.a; 1/ D 1, (d) gcd.ca; cb/ D jcj gcd.a; b/. E XERCISE 1.10. Show that for all integers a; b with d WD gcd.a; b/ ¤ 0, we have gcd.a=d; b=d / D 1. E XERCISE 1.11. Let n be an integer. Show that if a; b are relatively prime integers, each of which divides n, then ab divides n. E XERCISE 1.12. Show that two integers are relatively prime if and only if there is no one prime that divides both of them. E XERCISE 1.13. Let a; b1 ; : : : ; bk be integers. Show that gcd.a; b1 bk / D 1 if and only if gcd.a; bi / D 1 for i D 1; : : : ; k. E XERCISE 1.14. Let p be a prime and k an integer, with 0 < k < p. Show that the binomial coefficient ! p pŠ D ; k kŠ.p k/Š which is an integer (see §A2), is divisible by p. E XERCISE 1.15. An integer a is called square-free if it is not divisible by the square of any integer greater than 1. Show that (a) a is square-free if and only if a D ˙p1 pr , where the pi ’s are distinct primes; (b) every positive integer n can be expressed uniquely as n D ab 2 , where a and b are positive integers, and a is square-free. E XERCISE 1.16. For each positive integer m, let Im denote f0; : : : ; m a; b be positive integers, and consider the map
1g. Let
W Ib Ia ! Iab .s; t / 7! .as C bt / mod ab: Show is a bijection if and only if gcd.a; b/ D 1. E XERCISE 1.17. Let a; b; c be positive integers, with gcd.a; b/ D 1 and c .a 1/.b 1/. Show that there exist non-negative integers s; t such that c D asCbt .
10
Basic properties of the integers
E XERCISE 1.18. For each positive integer n, let Dn denote the set of positive divisors of n. Let n1 ; n2 be relatively prime, positive integers. Show that the sets Dn1 Dn2 and Dn1 n2 are in one-to-one correspondence, via the map that sends .d1 ; d2 / 2 Dn1 Dn2 to d1 d2 . 1.3 Some consequences of unique factorization The following theorem is a consequence of just the existence part of Theorem 1.3: Theorem 1.11. There are infinitely many primes. Proof. By way of contradiction, suppose that there were only finitely many primes; Q call them p1 ; : : : ; pk . Then set M WD kiD1 pi and N WD M C 1. Consider a prime p that divides N . There must be at least one such prime p, since N 2, and every positive integer can be written as a product of primes. Clearly, p cannot equal any of the pi ’s, since if it did, then p would divide M , and hence also divide N M D 1, which is impossible. Therefore, the prime p is not among p1 ; : : : ; pk , which contradicts our assumption that these are the only primes. For each prime p, we may define the function p , mapping non-zero integers to non-negative integers, as follows: for every integer n ¤ 0, if n D p e m, where p − m, then p .n/ WD e. We may then write the factorization of n into primes as Y p p .n/ ; nD˙ p
where the product is over all primes p; although syntactically this is an infinite product, all but finitely many of its terms are equal to 1, and so this expression makes sense. Observe that if a and b are non-zero integers, then p .a b/ D p .a/ C p .b/ for all primes p;
(1.3)
a j b ” p .a/ p .b/ for all primes p:
(1.4)
and
From this, it is clear that gcd.a; b/ D
Y
p min.p .a/;p .b// :
p
Least common multiples. For a; b 2 Z, a common multiple of a and b is an integer m such that a j m and b j m; moreover, such an m is the least common multiple of a and b if m is non-negative and m divides all common multiples of a and b. It is easy to see that the least common multiple exists and is unique,
1.3 Some consequences of unique factorization
11
and we denote the least common multiple of a and b by lcm.a; b/. Indeed, for all a; b 2 Z, if either a or b are zero, the only common multiple of a and b is 0, and so lcm.a; b/ D 0; otherwise, if neither a nor b are zero, we have Y lcm.a; b/ D p max.p .a/;p .b// ; p
or equivalently, lcm.a; b/ may be characterized as the smallest positive integer divisible by both a and b. It is convenient to extend the domain of definition of p to include 0, defining p .0/ WD 1. If we interpret expressions involving “1” appropriately (see Preliminaries), then for arbitrary a; b 2 Z, both (1.3) and (1.4) hold, and in addition, p .gcd.a; b// D min.p .a/; p .b// and p .lcm.a; b// D max.p .a/; p .b// for all primes p. Generalizing gcd’s and lcm’s to many integers. It is easy to generalize the notions of greatest common divisor and least common multiple from two integers to many integers. Let a1 ; : : : ; ak be integers. We call d 2 Z a common divisor of a1 ; : : : ; ak if d j ai for i D 1; : : : ; k; moreover, we call such a d the greatest common divisor of a1 ; : : : ; ak if d is non-negative and all other common divisors of a1 ; : : : ; ak divide d . The greatest common divisor of a1 ; : : : ; ak is denoted gcd.a1 ; : : : ; ak / and is the unique non-negative integer d satisfying p .d / D min.p .a1 /; : : : ; p .ak // for all primes p: Analogously, we call m 2 Z a common multiple of a1 ; : : : ; ak if ai j m for i D 1; : : : ; k; moreover, such an m is called the least common multiple of a1 ; : : : ; ak if m divides all common multiples of a1 ; : : : ; ak . The least common multiple of a1 ; : : : ; ak is denoted lcm.a1 ; : : : ; ak / and is the unique non-negative integer m satisfying p .m/ D max.p .a1 /; : : : ; p .ak // for all primes p: Finally, we say that the family fai gkiD1 is pairwise relatively prime if gcd.ai ; aj / D 1 for all indices i; j with i ¤ j . Certainly, if fai gkiD1 is pairwise relatively prime, and k > 1, then gcd.a1 ; : : : ; ak / D 1; however, gcd.a1 ; : : : ; ak / D 1 does not imply that fai gkiD1 is pairwise relatively prime. Rational numbers. Consider now the rational numbers Q D fa=b W a; b 2 Z; b ¤ 0g. Given any rational number a=b, if we set d WD gcd.a; b/, and define the integers a0 WD a=d and b0 WD b=d , then we have a=b D a0 =b0 and gcd.a0 ; b0 / D 1. Moreover, if a1 =b1 D a0 =b0 , then we have a1 b0 D a0 b1 , and so b0 j a0 b1 , and since gcd.a0 ; b0 / D 1, we see that b0 j b1 ; if b1 D b0 c, it
12
Basic properties of the integers
follows that a1 D a0 c. Thus, we can represent every rational number as a fraction in lowest terms, that is, a fraction of the form a0 =b0 where a0 and b0 are relatively prime; moreover, the values of a0 and b0 are uniquely determined up to sign, and every other fraction that represents the same rational number is of the form a0 c =b0 c, for some non-zero integer c. E XERCISE 1.19. Let n be an integer. Generalizing Exercise 1.11, show that if fai gkiD1 is a pairwise relatively prime family of integers, where each ai divides n, Q then their product kiD1 ai also divides n. E XERCISE 1.20. Show that for all integers a; b; c, we have (a) lcm.a; b/ D lcm.b; a/, (b) lcm.a; b/ D jaj ” b j a, (c) lcm.a; a/ D lcm.a; 1/ D jaj, (d) lcm.ca; cb/ D jcj lcm.a; b/. E XERCISE 1.21. Show that for all integers a; b, we have (a) gcd.a; b/ lcm.a; b/ D jabj, (b) gcd.a; b/ D 1 H) lcm.a; b/ D jabj. E XERCISE 1.22. Let a1 ; : : : ; ak 2 Z with k > 1. Show that gcd.a1 ; : : : ; ak / D gcd.a1 ; gcd.a2 ; : : : ; ak // D gcd.gcd.a1 ; : : : ; ak lcm.a1 ; : : : ; ak / D lcm.a1 ; lcm.a2 ; : : : ; ak // D lcm.lcm.a1 ; : : : ; ak
1 /; ak /; 1 /; ak /:
E XERCISE 1.23. Let a1 ; : : : ; ak 2 Z with d WD gcd.a1 ; : : : ; ak /. Show that d Z D a1 Z C C ak Z; in particular, there exist integers z1 ; : : : ; zk such that d D a1 z1 C C ak zk : E XERCISE 1.24. Show that if fai gkiD1 is a pairwise relatively prime family of integers, then lcm.a1 ; : : : ; ak / D ja1 ak j. E XERCISE 1.25. Show that every non-zero x 2 Q can be expressed as x D ˙p1e1 prer ; where the pi ’s are distinct primes and the ei ’s are non-zero integers, and that this expression in unique up to a reordering of the primes. E XERCISE 1.26. Let n and k be positive integers, and suppose x 2 Q such that p x k D n for some x 2 Q. Show that x 2 Z. In other words, k n is either an integer or is irrational.
13
1.3 Some consequences of unique factorization
E XERCISE 1.27. Show that gcd.a C b; lcm.a; b// D gcd.a; b/ for all a; b 2 Z. E XERCISE 1.28. Show that for every positive integer k, there exist k consecutive composite integers. Thus, there are arbitrarily large gaps between primes. E XERCISE 1.29. Let a; b 2 Z and let p be a prime. Show that p .a C b/ minfp .a/; p .b/g, and that if p .a/ < p .b/, then p .a C b/ D p .a/. E XERCISE 1.30. For a given prime p, we may extend the domain of definition of p from Z to Q: for non-zero integers a; b, let us define p .a=b/ WD p .a/ p .b/. Show that: (a) this definition of p .a=b/ is unambiguous, in the sense that it does not depend on the particular choice of a and b; (b) for all x; y 2 Q, we have p .xy/ D p .x/ C p .y/; (c) for all x; y 2 Q, we have p .x C y/ minfp .x/; p .y/g, and if p .x/ < p .y/, then p .x C y/ D p .x/; Q (d) for all non-zero x 2 Q, we have x D ˙ p p p .x/ ; where the product is over all primes, and all but a finite number of terms in the product are equal to 1; (e) for all x 2 Q, we have x 2 Z if and only if p .x/ 0 for all primes p. E XERCISE 1.31. Let n be a positive integer, and let 2k be the highest power of 2 in the set S WD f1; : : : ; ng. Show that 2k does not divide any other element in S. P E XERCISE 1.32. Let n 2 Z with n > 1. Show that niD1 1= i is not an integer. E XERCISE 1.33. Let n be a positive integer, and let Cn denote the number of pairs of integers .a; b/ with a; b 2 f1; : : : ; ng and gcd.a; b/ D 1, and let Fn be the number of distinct rational numbers a=b, where 0 a < b n. (a) Show that Fn D .Cn C 1/=2. (b) Show that Cn n2 =4. Hint: first show that Cn n2 .1 P and then show that d 2 1=d 2 3=4.
P
d 2 1=d
2 /,
E XERCISE 1.34. This exercise develops a characterization of least common multiples in terms of ideals. (a) Arguing directly from the definition of an ideal, show that if I and J are ideals of Z, then so is I \ J . (b) Let a; b 2 Z, and consider the ideals I WD aZ and J WD bZ. By part (a), we know that I \J is an ideal. By Theorem 1.6, we know that I \J D mZ for some uniquely determined non-negative integer m. Show that m D lcm.a; b/.
2 Congruences
This chapter introduces the basic properties of congruences modulo n, along with the related notion of residue classes modulo n. Other items discussed include the Chinese remainder theorem, Euler’s phi function, Euler’s theorem, Fermat’s little theorem, quadratic residues, and finally, summations over divisors. 2.1 Equivalence relations Before discussing congruences, we review the definition and basic properties of equivalence relations. Let S be a set. A binary relation on S is called an equivalence relation if it is reflexive: a a for all a 2 S, symmetric: a b implies b a for all a; b 2 S , and transitive: a b and b c implies a c for all a; b; c 2 S. If is an equivalence relation on S , then for a 2 S one defines its equivalence class as the set fx 2 S W x ag. Theorem 2.1. Let be an equivalence relation on a set S , and for a 2 S , let Œa denote its equivalence class. Then for all a; b 2 S , we have (i) a 2 Œa; (ii) a 2 Œb implies Œa D Œb. Proof. (i) follows immediately from reflexivity. For (ii), suppose a 2 Œb, so that a b by definition. We want to show that Œa D Œb. To this end, consider any
14
2.2 Definitions and basic properties of congruences
15
x 2 S . We have x 2 Œa H) x a (by definition) H) x b (by transitivity, and since x a and a b) H) x 2 Œb: Thus, Œa Œb. By symmetry, we also have b a, and reversing the roles of a and b in the above argument, we see that Œb Œa. This theorem implies that each equivalence class is non-empty, and that each element of S belongs to a unique equivalence class; in other words, the distinct equivalence classes form a partition of S (see Preliminaries). A member of an equivalence class is called a representative of the class. E XERCISE 2.1. Consider the relations D, , and < on the set R. Which of these are equivalence relations? Explain your answers. E XERCISE 2.2. Let S WD R R n f.0; 0/g. For .x; y/; .x 0 ; y 0 / 2 S , let us say .x; y/ .x 0 ; y 0 / if there exists a real number > 0 such that .x; y/ D .x 0 ; y 0 /. Show that is an equivalence relation; moreover, show that each equivalence class contains a unique representative that lies on the unit circle (i.e., the set of points .x; y/ such that x 2 C y 2 D 1). 2.2 Definitions and basic properties of congruences Let n be a positive integer. For integers a and b, we say that a is congruent to b modulo n if n j .a b/, and we write a b .mod n/. If n − .a b/, then we write a 6 b .mod n/. Equivalently, a b .mod n/ if and only if a D b C ny for some y 2 Z. The relation a b .mod n/ is called a congruence relation, or simply, a congruence. The number n appearing in such congruences is called the modulus of the congruence. This usage of the “mod” notation as part of a congruence is not to be confused with the “mod” operation introduced in §1.1. If we view the modulus n as fixed, then the following theorem says that the binary relation “ .mod n/” is an equivalence relation on the set Z. Theorem 2.2. Let n be a positive integer. For all a; b; c 2 Z, we have: (i) a a .mod n/; (ii) a b .mod n/ implies b a .mod n/; (iii) a b .mod n/ and b c .mod n/ implies a c .mod n/. Proof. For (i), observe that n divides 0 D a
a. For (ii), observe that if n divides
16
a a
Congruences
b, then it also divides .a b/ D b a. For (iii), observe that if n divides b and b c, then it also divides .a b/ C .b c/ D a c.
Another key property of congruences is that they are “compatible” with integer addition and multiplication, in the following sense: Theorem 2.3. Let a; a0 ; b; b 0 ; n 2 Z with n > 0. If a a0 .mod n/ and b b 0 .mod n/; then a C b a0 C b 0 .mod n/ and a b a0 b 0 .mod n/: Proof. Suppose that a a0 .mod n/ and b b 0 .mod n/. This means that there exist integers x and y such that a D a0 C nx and b D b 0 C ny. Therefore, a C b D a0 C b 0 C n.x C y/; which proves the first congruence of the theorem, and ab D .a0 C nx/.b 0 C ny/ D a0 b 0 C n.a0 y C b 0 x C nxy/; which proves the second congruence. Theorems 2.2 and 2.3 allow one to work with congruence relations modulo n much as one would with ordinary equalities: one can add to, subtract from, or multiply both sides of a congruence modulo n by the same integer; also, if b is congruent to a modulo n, one may substitute b for a in any simple arithmetic expression (involving addition, subtraction, and multiplication) appearing in a congruence modulo n. Now suppose a is an arbitrary, fixed integer, and consider the set of integers z that satisfy the congruence z a .mod n/. Since z satisfies this congruence if and only if z D a C ny for some y 2 Z, we may apply Theorems 1.4 and 1.5 (with a as given, and b WD n) to deduce that every interval of n consecutive integers contains exactly one such z. This simple fact is of such fundamental importance that it deserves to be stated as a theorem: Theorem 2.4. Let a; n 2 Z with n > 0. Then there exists a unique integer z such that z a .mod n/ and 0 z < n, namely, z WD a mod n. More generally, for every x 2 R, there exists a unique integer z 2 Œx; x C n/ such that z a .mod n/. Example 2.1. Let us find the set of solutions z to the congruence 3z C 4 6 .mod 7/:
(2.1)
2.2 Definitions and basic properties of congruences
17
Suppose that z is a solution to (2.1). Subtracting 4 from both sides of (2.1), we obtain 3z 2 .mod 7/:
(2.2)
Next, we would like to divide both sides of this congruence of 3, to get z by itself on the left-hand side. We cannot do this directly, but since 5 3 1 .mod 7/, we can achieve the same effect by multiplying both sides of (2.2) by 5. If we do this, and then replace 5 3 by 1, and 5 2 by 3, we obtain z 3 .mod 7/: Thus, if z is a solution to (2.1), we must have z 3 .mod 7/; conversely, one can verify that if z 3 .mod 7/, then (2.1) holds. We conclude that the integers z that are solutions to (2.1) are precisely those integers that are congruent to 3 modulo 7, which we can list as follows: : : : ; 18; 11; 4; 3; 10; 17; 24; : : : In the next section, we shall give a systematic treatment of the problem of solving linear congruences, such as the one appearing in the previous example. E XERCISE 2.3. Let a; b; n 2 Z with n > 0. Show that a b .mod n/ if and only if .a mod n/ D .b mod n/. E XERCISE 2.4. Let a; b; n 2 Z with n > 0 and a b .mod n/. Also, let c0 ; c1 ; : : : ; ck 2 Z. Show that c0 C c1 a C C ck ak c0 C c1 b C C ck b k .mod n/: E XERCISE 2.5. Let a; b; n; n0 2 Z with n > 0, n0 > 0, and n0 j n. Show that if a b .mod n/, then a b .mod n0 /. E XERCISE 2.6. Let a; b; n; n0 2 Z with n > 0, n0 > 0, and gcd.n; n0 / D 1. Show that if a b .mod n/ and a b .mod n0 /, then a b .mod nn0 /. E XERCISE 2.7. Let a; b; n 2 Z with n > 0 and a b .mod n/. Show that gcd.a; n/ D gcd.b; n/. E XERCISE 2.8. Let a be a positive integer whose base-10 representation is a D .ak 1 a1 a0 /10 . Let b be the sum of the decimal digits of a; that is, let b WD a0 C a1 C C ak 1 . Show that a b .mod 9/. From this, justify the usual “rules of thumb” for determining divisibility by 9 and 3: a is divisible by 9 (respectively, 3) if and only if the sum of the decimal digits of a is divisible by 9 (respectively, 3).
18
Congruences
E XERCISE 2.9. Let e be a positive integer. For a 2 f0; : : : ; 2e 1g, let aQ denote the integer obtained by inverting the bits in the e-bit, binary representation of a (note that aQ 2 f0; : : : ; 2e 1g). Show that aQ C 1 a .mod 2e /. This justifies the usual rule for computing negatives in 2’s complement arithmetic (which is really just arithmetic modulo 2e ). E XERCISE 2.10. Show that the equation 7y 3 C 2 D z 3 has no solutions y; z 2 Z. E XERCISE 2.11. Show that there are 14 distinct, possible, yearly (Gregorian) calendars, and show that all 14 calendars actually occur. 2.3 Solving linear congruences In this section, we consider the general problem of solving linear congruences. More precisely, for a given positive integer n, and arbitrary integers a and b, we wish to determine the set of integers z that satisfy the congruence az b .mod n/:
(2.3)
Observe that if (2.3) has a solution z, and if z z 0 .mod n/, then z 0 is also a solution to (2.3). However, (2.3) may or may not have a solution, and if it does, such solutions may or may not be uniquely determined modulo n. The following theorem precisely characterizes the set of solutions of (2.3); basically, it says that (2.3) has a solution if and only if d WD gcd.a; n/ divides b, in which case the solution is uniquely determined modulo n=d . Theorem 2.5. Let a; n 2 Z with n > 0, and let d WD gcd.a; n/. (i) For every b 2 Z, the congruence az b .mod n/ has a solution z 2 Z if and only if d j b. (ii) For every z 2 Z, we have az 0 .mod n/ if and only if z 0 .mod n=d /. (iii) For all z; z 0 2 Z, we have az az 0 .mod n/ if and only if z z 0 .mod n=d /. Proof. For (i), let b 2 Z be given. Then we have az b .mod n/ for some z 2 Z ” az D b C ny for some z; y 2 Z (by definition of congruence) ” az
ny D b for some z; y 2 Z
” d j b (by Theorem 1.8): For (ii), we have n j az ” n=d j .a=d /z ” n=d j z:
19
2.3 Solving linear congruences
All of these implications follow rather trivially from the definition of divisibility, except that for the implication n=d j .a=d /z H) n=d j z, we use Theorem 1.9 and the fact that gcd.a=d; n=d / D 1. For (iii), we have az az 0 .mod n/ ” a.z ” z
z 0 / 0 .mod n/ z 0 0 .mod n=d / (by part (ii))
” z z 0 .mod n=d /: We can restate Theorem 2.5 in more concrete terms as follows. Let a; n 2 Z with n > 0, and let d WD gcd.a; n/. Let In WD f0; : : : ; n 1g and consider the “multiplication by a” map a W I n ! I n z 7! az mod n: The image of a consists of the n=d integers i d .i D 0; : : : ; n=d
1/:
Moreover, every element b in the image of a has precisely d pre-images z0 C j .n=d / .j D 0; : : : ; d where z0 2 f0; : : : ; n=d are relatively prime.
1/;
1g. In particular, a is a bijection if and only if a and n
Example 2.2. The following table illustrates what Theorem 2.5 says for n D 15 and a D 1; 2; 3; 4; 5; 6.
2z 3z 4z 5z 6z
z mod 15 mod 15 mod 15 mod 15 mod 15
0 0 0 0 0 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 2 4 6 8 10 12 14 1 3 5 7 9 11 13 3 6 9 12 0 3 6 9 12 0 3 6 9 12 4 8 12 1 5 9 13 2 6 10 14 3 7 11 5 10 0 5 10 0 5 10 0 5 10 0 5 10 6 12 3 9 0 6 12 3 9 0 6 12 3 9
In the second row, we are looking at the values 2z mod 15, and we see that this row is just a permutation of the first row. So for every b, there exists a unique z such that 2z b .mod 15/. This is implied by the fact that gcd.2; 15/ D 1. In the third row, the only numbers hit are the multiples of 3, which follows from the fact that gcd.3; 15/ D 3. Also note that the pattern in this row repeats every five columns; that is, 3z 3z 0 .mod 15/ if and only if z z 0 .mod 5/.
20
Congruences
In the fourth row, we again see a permutation of the first row, which follows from the fact that gcd.4; 15/ D 1. In the fifth row, the only numbers hit are the multiples of 5, which follows from the fact that gcd.5; 15/ D 5. Also note that the pattern in this row repeats every three columns; that is, 5z 5z 0 .mod 15/ if and only if z z 0 .mod 3/. In the sixth row, since gcd.6; 15/ D 3, we see a permutation of the third row. The pattern repeats after five columns, although the pattern is a permutation of the pattern in the third row. We develop some further consequences of Theorem 2.5. A cancellation law. Let a; n 2 Z with n > 0. Part (iii) of Theorem 2.5 gives us a cancellation law for congruences: if gcd.a; n/ D 1 and az az 0 .mod n/, then z z 0 .mod n/. Example 2.3. Observe that 5 2 5 . 4/ .mod 6/:
(2.4)
Theorem 2.5 tells us that since gcd.5; 6/ D 1, we may cancel the common factor of 5 from both sides of (2.4), obtaining 2 4 .mod 6/, which one can also verify directly. Next observe that 3 5 3 3 .mod 6/:
(2.5)
We cannot simply cancel the common factor of 3 from both sides of (2.5); indeed, 5 6 3 .mod 6/. However, gcd.3; 6/ D 3, and as Theorem 2.5 guarantees, we do indeed have 5 3 .mod 2/. Modular inverses. Again, let a; n 2 Z with n > 0. We say that z 2 Z is a multiplicative inverse of a modulo n if az 1 .mod n/. Part (i) of Theorem 2.5 says that a has a multiplicative inverse modulo n if and only if gcd.a; n/ D 1. Moreover, part (iii) of Theorem 2.5 says that the multiplicative inverse of a, if it exists, is uniquely determined modulo n; that is, if z and z 0 are multiplicative inverses of a modulo n, then z z 0 .mod n/. Note that if z is a multiplicative inverse of a modulo n, then a is a multiplicative inverse of z modulo n. Also note that if a a0 .mod n/, then z is a multiplicative inverse of a modulo n if and only if z is a multiplicative inverse of a0 modulo n. Now suppose that a; b; n 2 Z with n > 0, a ¤ 0, and gcd.a; n/ D 1. Theorem 2.5 says that there exists a unique integer z satisfying az b .mod n/ and 0 z < n: Setting s WD b=a 2 Q, we may generalize the “mod” operation, defining s mod n
2.4 The Chinese remainder theorem
21
to be this value z. As the reader may easily verify, this definition of s mod n does not depend on the particular choice of fraction used to represent the rational number s. With this notation, we can simply write a 1 mod n to denote the unique multiplicative inverse of a modulo n that lies in the interval 0; : : : ; n 1. Example 2.4. Looking back at the table in Example 2.2, we see that 2
1
mod 15 D 8 and 4
1
mod 15 D 4;
and that neither 3, 5, nor 6 have modular inverses modulo 15. E XERCISE 2.12. Let a1 ; : : : ; ak ; b; n be integers with n > 0, and let d WD gcd.a1 ; : : : ; ak ; n/. Show that the congruence a1 z1 C C ak zk b .mod n/ has a solution z1 ; : : : ; zk 2 Z if and only if d j b. E XERCISE 2.13. Let p be a prime, and let a; b; c; e be integers, such that e > 0, a 6 0 .mod p e /, and 0 c < p e . Let N be the number of integers z 2 f0; : : : ; p 2e 1g such that j ı k .az C b/ mod p 2e p e D c: Show that N D p e . 2.4 The Chinese remainder theorem Next, we consider systems of linear congruences with respect to moduli that are relatively prime in pairs. The result we state here is known as the Chinese remainder theorem, and is extremely useful in a number of contexts. Theorem 2.6 (Chinese remainder theorem). Let fni gkiD1 be a pairwise relatively prime family of positive integers, and let a1 ; : : : ; ak be arbitrary integers. Then there exists a solution a 2 Z to the system of congruences a ai .mod ni / .i D 1; : : : ; k/: Moreover, any a0 2 Z is a solution to this system of congruences if and only if Q a a0 .mod n/, where n WD kiD1 ni . Proof. To prove the existence of a solution a to the system of congruences, we first show how to construct integers e1 ; : : : ; ek such that for i; j D 1; : : : ; k, we have 1 .mod ni / if j D i , ej (2.6) 0 .mod ni / if j ¤ i .
22
Congruences
If we do this, then setting a WD
k X
ai ei ;
i D1
one sees that for j D 1; : : : ; k, we have a
k X
ai ei aj .mod nj /;
i D1
since all the terms in this sum are zero modulo nj , except for the term i D j , which is congruent to aj modulo nj . Q To construct e1 ; : : : ; ek satisfying (2.6), let n WD kiD1 ni as in the statement of the theorem, and for i D 1; : : : ; k, let ni WD n=ni ; that is, ni is the product of all the moduli nj with j ¤ i . From the fact that fni gkiD1 is pairwise relatively prime, it follows that for i D 1; : : : ; k, we have gcd.ni ; ni / D 1, and so we may define ti WD .ni / 1 mod ni and ei WD ni ti . One sees that ei 1 .mod ni /, while for j ¤ i , we have ni j nj , and so ej 0 .mod ni /. Thus, (2.6) is satisfied. That proves the existence of a solution a to the given system of congruences. If a a0 .mod n/, then since ni j n for i D 1; : : : ; k, we see that a0 a ai .mod ni / for i D 1; : : : ; k, and so a0 also solves the system of congruences. Finally, if a0 is a solution to the given system of congruences, then a ai 0 a .mod ni / for i D 1; : : : ; k. Thus, ni j .a a0 / for i D 1; : : : ; k. Since fni gkiD1 is pairwise relatively prime, this implies that n j .a a0 /, or equivalently, a a0 .mod n/. We can restate Theorem 2.6 in more concrete terms, as follows. For each positive integer m, let Im denote f0; : : : ; m 1g. Suppose fni gkiD1 is a pairwise relatively prime family of positive integers, and set n WD n1 nk . Then the map W In ! In1 Ink a 7! .a mod n1 ; : : : ; a mod nk / is a bijection. Example 2.5. The following table illustrates what Theorem 2.6 says for n1 D 3 and n2 D 5. a 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 a mod 3 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 a mod 5 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 We see that as a ranges from 0 to 14, the pairs .a mod 3; a mod 5/ range over
2.5 Residue classes
23
all pairs .a1 ; a2 / with a1 2 f0; 1; 2g and a2 2 f0; : : : ; 4g, with every pair being hit exactly once. E XERCISE 2.14. Compute the values e1 ; e2 ; e3 in the proof of Theorem 2.6 in the case where k D 3, n1 D 3, n2 D 5, and n3 D 7. Also, find an integer a such that a 1 .mod 3/, a 1 .mod 5/, and a 5 .mod 7/. E XERCISE 2.15. If you want to show that you are a real nerd, here is an ageguessing game you might play at a party. You ask a fellow party-goer to divide his age by each of the numbers 3, 4, and 5, and tell you the remainders. Show how to use this information to determine their age. E XERCISE 2.16. Let fni gkiD1 be a pairwise relatively prime family of positive integers. Let a1 ; : : : ; ak and b1 ; : : : ; bk be integers, and set di WD gcd.ai ; ni / for i D 1; : : : ; k. Show that there exists an integer z such that ai z bi .mod ni / for i D 1; : : : ; k if and only if di j bi for i D 1; : : : ; k. E XERCISE 2.17. For each prime p, let p ./ be defined as in §1.3. Let p1 ; : : : ; pr be distinct primes, a1 ; : : : ; ar be arbitrary integers, and e1 ; : : : ; er be arbitrary nonnegative integers. Show that there exists an integer a such that pi .a ai / D ei for i D 1; : : : ; r. E XERCISE 2.18. Suppose n1 and n2 are positive integers, and let d WD gcd.n1 ; n2 /. Let a1 and a2 be arbitrary integers. Show that there exists an integer a such that a a1 .mod n1 / and a a2 .mod n2 / if and only if a1 a2 .mod d /. 2.5 Residue classes As we already observed in Theorem 2.2, for any fixed positive integer n, the binary relation “ .mod n/” is an equivalence relation on the set Z. As such, this relation partitions the set Z into equivalence classes. We denote the equivalence class containing the integer a by Œan , and when n is clear from context, we simply write Œa. By definition, we have z 2 Œa ” z a .mod n/ ” z D a C ny for some y 2 Z; and hence Œa D a C nZ WD fa C ny W y 2 Zg: Historically, these equivalence classes are called residue classes modulo n, and we shall adopt this terminology here as well. Note that a given residue class modulo n has many different “names”; for example, the residue class Œn 1 is the same as
24
Congruences
the residue class Œ 1. Any member of a residue class is called a representative of that class. We define Zn to be the set of residue classes modulo n. The following is simply a restatement of Theorem 2.4: Theorem 2.7. Let n be a positive integer. Then Zn consists of the n distinct residue classes Œ0; Œ1; : : : ; Œn 1. Moreover, for every x 2 R, each residue class modulo n contains a unique representative in the interval Œx; x C n/. When working with residue classes modulo n, one often has in mind a particular set of representatives. Typically, one works with the set of representatives f0; 1; : : : ; n 1g. However, sometimes it is convenient to work with another set of representatives, such as the representatives in the interval Œ n=2; n=2/. In this case, if n is odd, we can list the elements of Zn as Œ .n
1/=2; : : : ; Œ 1; Œ0; Œ1; : : : ; Œ.n
1/=2;
and when n is even, we can list the elements of Zn as Œ n=2; : : : ; Œ 1; Œ0; Œ1; : : : ; Œn=2
1:
We can “equip” Zn with binary operations defining addition and multiplication in a natural way as follows: for a; b 2 Z, we define Œa C Œb WD Œa C b; Œa Œb WD Œa b: Of course, one has to check this definition is unambiguous, in the sense that the sum or product of two residue classes should not depend on which particular representatives of the classes are chosen in the above definitions. More precisely, one must check that if Œa D Œa0 and Œb D Œb 0 , then Œa C b D Œa0 C b 0 and Œa b D Œa0 b 0 . However, this property follows immediately from Theorem 2.3. Observe that for all a; b; c 2 Z, we have Œa C Œb D Œc ” a C b c .mod n/; and Œa Œb D Œc ” a b c .mod n/;
25
2.5 Residue classes
Example 2.6. Consider the residue classes modulo 6. These are as follows: Œ0 D f: : : ; 12; 6; 0; 6; 12; : : :g Œ1 D f: : : ; 11; 5; 1; 7; 13; : : :g Œ2 D f: : : ; 10; 4; 2; 8; 14; : : :g Œ3 D f: : : ; 9; 3; 3; 9; 15; : : :g Œ4 D f: : : ; 8; 2; 4; 10; 16; : : :g Œ5 D f: : : ; 7; 1; 5; 11; 17; : : :g Let us write down the addition and multiplication tables for Z6 . The addition table looks like this: C Œ0 Œ1 Œ2 Œ3 Œ4 Œ5
Œ0 Œ0 Œ1 Œ2 Œ3 Œ4 Œ5
Œ1 Œ1 Œ2 Œ3 Œ4 Œ5 Œ0
Œ2 Œ2 Œ3 Œ4 Œ5 Œ0 Œ1
Œ3 Œ3 Œ4 Œ5 Œ0 Œ1 Œ2
Œ4 Œ4 Œ5 Œ0 Œ1 Œ2 Œ3
Œ5 Œ5 Œ0 Œ1 Œ2 Œ3 Œ4
Œ2 Œ0 Œ2 Œ4 Œ0 Œ2 Œ4
Œ3 Œ0 Œ3 Œ0 Œ3 Œ0 Œ3
Œ4 Œ0 Œ4 Œ2 Œ0 Œ4 Œ2
Œ5 Œ0 Œ5 Œ4 Œ3 Œ2 Œ1
The multiplication table looks like this: Œ0 Œ1 Œ2 Œ3 Œ4 Œ5
Œ0 Œ0 Œ0 Œ0 Œ0 Œ0 Œ0
Œ1 Œ0 Œ1 Œ2 Œ3 Œ4 Œ5
Instead of using representatives in the interval Œ0; 6/, we could just as well use representatives from another interval, such as Œ 3; 3/. Then, instead of naming the residue classes Œ0; Œ1; Œ2; Œ3; Œ4; Œ5, we would name them Œ 3; Œ 2; Œ 1; Œ0; Œ1; Œ2. Observe that Œ 3 D Œ3, Œ 2 D Œ4, and Œ 1 D Œ5.
Algebraic properties These operations on Zn yield a very natural algebraic structure. For example, addition and multiplication are commutative and associative; that is, for all
26
Congruences
˛; ˇ; 2 Zn , we have ˛ C ˇ D ˇ C ˛; .˛ C ˇ/ C D ˛ C .ˇ C /; ˛ˇ D ˇ˛; .˛ˇ/ D ˛.ˇ /: Note that we have adopted here the usual convention of writing ˛ˇ in place of ˛ ˇ. Furthermore, multiplication distributes over addition; that is, for all ˛; ˇ; 2 Zn , we have ˛.ˇ C / D ˛ˇ C ˛ : All of these properties follow from the definitions, and the corresponding properties for Z; for example, the fact that addition in Zn is commutative may be seen as follows: if ˛ D Œa and ˇ D Œb, then ˛ C ˇ D Œa C Œb D Œa C b D Œb C a D Œb C Œa D ˇ C ˛: Because addition and multiplication in Zn are associative, for ˛1 ; : : : ; ˛k 2 Zn , we may write the sum ˛1 C C ˛k and the product ˛1 ˛k without any parenthesis, and there is no ambiguity; moreover, since both addition and multiplication are commutative, we may rearrange the terms in such sums and products without changing their values. Further, from the distributive law, for all ˇ 2 Zn , we have ˇ.˛1 C C ˛k / D ˇ˛1 C C ˇ˛k : The residue class Œ0 acts as an additive identity; that is, for all ˛ 2 Zn , we have ˛ C Œ0 D ˛; indeed, if ˛ D Œa, then a C 0 a .mod n/. Moreover, Œ0 is the only element of Zn that acts as an additive identity; indeed, if a C z a .mod n/ holds for all integers a, then it holds in particular for a D 0, which implies z 0 .mod n/. The residue class Œ0 also has the property that ˛ Œ0 D Œ0 for all ˛ 2 Zn . Every ˛ 2 Zn has an additive inverse, that is, an element ˇ 2 Zn such that ˛ C ˇ D Œ0; indeed, if ˛ D Œa, then clearly ˇ WD Œ a does the job, since a C . a/ 0 .mod n/. Moreover, ˛ has at most one additive inverse; indeed, if a C z 0 .mod n/, then subtracting a from both sides of this congruence yields z a .mod n/. We naturally denote the additive inverse of ˛ by ˛. Observe that the additive inverse of ˛ is ˛; that is . ˛/ D ˛. Also, we have the identities .˛ C ˇ/ D . ˛/ C . ˇ/; . ˛/ˇ D
.˛ˇ/ D ˛. ˇ/; . ˛/. ˇ/ D ˛ˇ:
For ˛; ˇ 2 Zn , we naturally write ˛ ˇ for ˛ C . ˇ/. The residue class Œ1 acts as a multiplicative identity; that is, for all ˛ 2 Zn , we have ˛ Œ1 D ˛; indeed, if ˛ D Œa, then a 1 a .mod n/. Moreover, Œ1 is the only element of Zn that acts as a multiplicative identity; indeed, if a z
27
2.5 Residue classes
a .mod n/ holds for all integers a, then in particular, it holds for a D 1, which implies z 1 .mod n/. For ˛ 2 Zn , we call ˇ 2 Zn a multiplicative inverse of ˛ if ˛ˇ D Œ1. Not all ˛ 2 Zn have multiplicative inverses. If ˛ D Œa and ˇ D Œb, then ˇ is a multiplicative inverse of ˛ if and only if ab 1 .mod n/. Theorem 2.5 implies that ˛ has a multiplicative inverse if and only if gcd.a; n/ D 1, and that if it exists, it is unique. When it exists, we denote the multiplicative inverse of ˛ by ˛ 1 . We define Zn to be the set of elements of Zn that have a multiplicative inverse. By the above discussion, we have Zn D fŒa W a D 0; : : : ; n
1; gcd.a; n/ D 1g:
If n is prime, then gcd.a; n/ D 1 for a D 1; : : : ; n 1, and we see that Zn D Zn n fŒ0g. If n is composite, then Zn ¨ Zn n fŒ0g; for example, if d j n with 1 < d < n, we see that Œd is not zero, nor does it belong to Zn . Observe that if ˛; ˇ 2 Zn , then so are ˛ 1 and ˛ˇ; indeed, .˛
1
/
1
D ˛ and .˛ˇ/
1
D˛
1
ˇ
1
:
For ˛ 2 Zn and ˇ 2 Zn , we naturally write ˛=ˇ for ˛ˇ 1 . Suppose ˛; ˇ; are elements of Zn that satisfy the equation ˛ˇ D ˛ : If ˛ 2 Zn , we may multiply both sides of this equation by ˛
1
to infer that
ˇ D : This is the cancellation law for Zn . We stress the requirement that ˛ 2 Zn , and not just ˛ ¤ Œ0. Indeed, consider any ˛ 2 Zn n Zn . Then we have ˛ D Œa with d WD gcd.a; n/ > 1. Setting ˇ WD Œn=d and WD Œ0, we see that ˛ˇ D ˛ and ˇ ¤ : Example 2.7. We list the elements of Z15 , and for each ˛ 2 Z15 , we also give ˛ 1. ˛ ˛
1
[1] [1]
[2] [8]
[4] [4]
[7] [13]
[8] [2]
[11] [11]
[13] [7]
[14] [14]
Notational conventions P For ˛1 ; : : : ; ˛k 2 Zn , we may naturally write their sum as kiD1 ˛i . By convenPk Pk tion, this sum is Œ0 when k D 0. It is easy to see that i D1 ˛i D i D1 . ˛i /;
28
Congruences
that is, the additive inverse of the sum is the sum of the additive inverses. In the P special case where all the ˛i ’s have the same value ˛, we define k˛ WD kiD1 ˛; thus, 0˛ D Œ0, 1˛ D ˛, 2˛ D ˛ C ˛, 3˛ D ˛ C ˛ C ˛, and so on. The additive inverse of k˛ is k. ˛/, which we may also write as . k/˛; thus, . 1/˛ D ˛, . 2/˛ D . ˛/ C . ˛/ D .˛ C ˛/, and so on. Therefore, the notation k˛ is defined for all integers k. Note that for all integers k and a, we have kŒa D Œka D ŒkŒa. For all ˛; ˇ 2 Zn and k; ` 2 Z, we have the identities: k.`˛/ D .k`/˛ D `.k˛/; .k C `/˛ D k˛ C `˛; k.˛ C ˇ/ D k˛ C kˇ; .k˛/ˇ D k.˛ˇ/ D ˛.kˇ/: Q Analogously, for ˛1 ; : : : ; ˛k 2 Zn , we may write their product as kiD1 ˛i . By convention, this product is Œ1 when k D 0. It is easy to see that if all of the Q ˛i ’s belong to Zn , then so does their product, and in particular, . kiD1 ˛i / 1 D Qk 1 i D1 ˛i ; that is, the multiplicative inverse of the product is the product of the multiplicative inverses. In the special case where all the ˛i ’s have the same value Q ˛, we define ˛ k WD kiD1 ˛; thus, ˛ 0 D Œ1, ˛ 1 D ˛, ˛ 2 D ˛˛, ˛ 3 D ˛˛˛, and so on. If ˛ 2 Zn , then the multiplicative inverse of ˛ k is .˛ 1 /k , which we may also write as ˛ k ; for example, ˛ 2 D ˛ 1 ˛ 1 D .˛˛/ 1 . Therefore, when ˛ 2 Zn , the notation ˛ k is defined for all integers k. For all ˛; ˇ 2 Zn and all non-negative integers k and `, we have the identities: .˛ ` /k D ˛ k` D .˛ k /` ; ˛ kC` D ˛ k ˛ ` ; .˛ˇ/k D ˛ k ˇ k :
(2.7)
If ˛; ˇ 2 Zn , the identities in (2.7) hold for all k; ` 2 Z. One last notational convention. As already mentioned, when the modulus n is clear from context, we usually write Œa instead of Œan . Although we want to maintain a clear distinction between integers and their residue classes, occasionally even the notation Œa is not only redundant, but distracting; in such situations, we may simply write a instead of Œa. For example, for every ˛ 2 Zn , we have the identity .˛ C Œ1n /.˛ Œ1n / D ˛ 2 Œ1n ; which we may write more simply as .˛ C Œ1/.˛ Œ1/ D ˛ 2 Œ1; or even more simply, and hopefully more clearly, as .˛ C 1/.˛ 1/ D ˛ 2 1: Here, the only reasonable interpretation of the symbol “1” is Œ1, and so there can be no confusion. Summary In summary, algebraic expressions involving residue classes may be manipulated in much the same way as expressions involving ordinary numbers. Extra complications arise only because when n is composite, some non-zero elements of Zn do
2.5 Residue classes
29
not have multiplicative inverses, and the usual cancellation law does not apply for such elements. In general, one has a choice between working with congruences modulo n, or with the algebraic structure Zn ; ultimately, the choice is one of taste and convenience, and it depends on what one prefers to treat as “first class objects”: integers and congruence relations, or elements of Zn . An alternative, and somewhat more concrete, approach to defining Zn is to directly define it to consist of the n “symbols” Œ0; Œ1; : : : ; Œn 1, with addition and multiplication defined as Œa C Œb WD Œ.a C b/ mod n; Œa Œb WD Œ.a b/ mod n; for a; b 2 f0; : : : ; n 1g. Such a definition is equivalent to the one we have given here. One should keep this alternative characterization of Zn in mind; however, we prefer the characterization in terms of residue classes, as it is mathematically more elegant, and is usually more convenient to work with. The Chinese remainder map We close this section with a reinterpretation of the Chinese remainder theorem (Theorem 2.6) in terms of residue classes. Theorem 2.8 (Chinese remainder map). Let fni gkiD1 be a pairwise relatively Q prime family of positive integers, and let n WD kiD1 ni . Define the map W
Zn ! Zn1 Znk Œan 7! .Œan1 ; : : : ; Œank /:
(i) The definition of is unambiguous. (ii) is bijective. (iii) For all ˛; ˇ 2 Zn , if .˛/ D .˛1 ; : : : ; ˛k / and .ˇ/ D .ˇ1 ; : : : ; ˇk /, then (a) .˛ C ˇ/ D .˛1 C ˇ1 ; : : : ; ˛k C ˇk /, (b) . ˛/ D . ˛1 ; : : : ; ˛k /, (c) .˛ˇ/ D .˛1 ˇ1 ; : : : ; ˛k ˇk /, (d) ˛ 2 Zn if and only if ˛i 2 Zni for i D 1; : : : ; k, in which case .˛ 1 / D .˛1 1 ; : : : ; ˛k 1 /: Proof. For (i), note that a a0 .mod n/ implies a a0 .mod ni / for i D 1; : : : ; k, and so the definition of is unambiguous (it does not depend on the choice of a). (ii) follows directly from the statement of the Chinese remainder theorem.
30
Congruences
For (iii), let ˛ D Œan and ˇ D Œbn , so that for i D 1; : : : ; k, we have ˛i D Œani and ˇi D Œbni . Then we have .˛Cˇ/ D .ŒaCbn / D .ŒaCbn1 ; : : : ; ŒaCbnk / D .˛1 Cˇ1 ; : : : ; ˛k Cˇk /; . ˛/ D .Œ an / D .Œ an1 ; : : : ; Œ ank / D . ˛1 ; : : : ; ˛k /; and .˛ˇ/ D .Œabn / D .Œabn1 ; : : : ; Œabnk / D .˛1 ˇ1 ; : : : ; ˛k ˇk /: That proves parts (a), (b), and (c). For part (d), we have ˛ 2 Zn ” gcd.a; n/ D 1 ” gcd.a; ni / D 1 for i D 1; : : : ; k ” ˛i 2 Zni for i D 1; : : : ; k: Moreover, if ˛ 2 Zn and ˇ D ˛
1,
then
.˛1 ˇ1 ; : : : ; ˛k ˇk / D .˛ˇ/ D .Œ1n / D .Œ1n1 ; : : : ; Œ1nk /; and so for i D 1; : : : ; k, we have ˛i ˇi D Œ1ni , which is to say ˇi D ˛i 1 . Theorem 2.8 is very powerful conceptually, and is an indispensable tool in many situations. It says that if we want to understand what happens when we add or multiply ˛; ˇ 2 Zn , it suffices to understand what happens when we add or multiply their “components” ˛i ; ˇi 2 Zni . Typically, we choose n1 ; : : : ; nk to be primes or prime powers, which usually simplifies the analysis. We shall see many applications of this idea throughout the text. E XERCISE 2.19. Let W Zn ! Zn1 Znk be as in Theorem 2.8, and suppose that .˛/ D .˛1 ; : : : ; ˛k /. Show that for every non-negative integer m, we have .˛ m / D .˛1m ; : : : ; ˛km /. Moreover, if ˛ 2 Zn , show that this identity holds for all integers m. P P E XERCISE 2.20. Let p be an odd prime. Show that ˇ 2Zp ˇ 1 D ˇ 2Zp ˇ D 0. Pp 1 E XERCISE 2.21. Let p be an odd prime. Show that the numerator of i D1 1= i is divisible by p. E XERCISE 2.22. Suppose n is square-free (see Exercise 1.15), and let ˛; ˇ; 2 Zn . Show that ˛ 2 ˇ D ˛ 2 implies ˛ˇ D ˛ . 2.6 Euler’s phi function For each positive integer n, we define .n/ WD jZn j. Equivalently, .n/ is equal to the number of integers between 0 and n 1 that are relatively prime to n. For
31
2.6 Euler’s phi function
example, .1/ D 1, .2/ D 1, .3/ D 2, and .4/ D 2. The function is called Euler’s phi function (or Euler’s totient function). Using the Chinese remainder theorem, more specifically Theorem 2.8, it is easy to get a nice formula for .n/ in terms for the prime factorization of n, as we establish in the following sequence of theorems. Theorem 2.9. Let fni gkiD1 be a pairwise relatively prime family of positive inteQ gers, and let n WD kiD1 ni . Then .n/ D
k Y
.ni /:
i D1
Proof. Consider the map W Zn ! Zn1 Znk in Theorem 2.8. By parts (ii) and (iii.d) of that theorem, restricting to Zn yields a one-to-one correspondence between Zn and Zn1 Znk . The theorem now follows immediately. We already know that .p/ D p 1 for every prime p, since the integers 1; : : : ; p 1 are not divisible by p, and hence are relatively prime to p. The next theorem generalizes this, giving us a formula for Euler’s phi function at prime powers. Theorem 2.10. Let p be a prime and e be a positive integer. Then .p e / D p e
1
.p
Proof. The multiples of p among 0; 1; : : : ; p e 0 p; 1 p; : : : ; .p e of which there are precisely p e
1.
1/: 1 are
1
1/ p;
Thus, .p e / D p e
pe
1
D pe
1 .p
1/:
If n D p1e1 prer is the factorization of n into primes, then the family of prime powers fpiei griD1 is pairwise relatively prime, and so Theorem 2.9 implies .n/ D .p1e1 / .prer /. Combining this with Theorem 2.10, we have: Theorem 2.11. If n D p1e1 prer is the factorization of n into primes, then .n/ D
r Y i D1
piei
1
.pi
1/ D n
r Y
.1
1=pi /:
i D1
E XERCISE 2.23. Show that .nm/ D gcd.n; m/ .lcm.n; m//. E XERCISE 2.24. Show that if n is divisible by r distinct odd primes, then 2r j .n/.
32
Congruences
E XERCISE 2.25. For every positive integer n, define 2 .n/ to be the number of integers a 2 f0; : : : ; n 1g such that gcd.a; n/ D gcd.a C 1; n/ D 1. Suppose n D Q p1e1 prer is the factorization of n into primes. Show that 2 .n/ D n riD1 .1 2=pi /: 2.7 Euler’s theorem and Fermat’s little theorem Let n be a positive integer, and let ˛ 2 Zn . Consider the sequence of powers of ˛: 1 D ˛0; ˛1; ˛2; : : : : Since each such power is an element of Zn , and since Zn is a finite set, this sequence of powers must start to repeat at some point; that is, there must be a positive integer k such that ˛ k D ˛ i for some i D 0; : : : ; k 1. Let us assume that k is chosen to be the smallest such positive integer. We claim that i D 0, or equivalently, ˛ k D 1. To see this, suppose by way of contradiction that ˛ k D ˛ i , for some i D 1; : : : ; k 1. Then we can cancel ˛ from both sides of the equation ˛ k D ˛ i , obtaining ˛ k 1 D ˛ i 1 , and this contradicts the minimality of k. This value k is called the multiplicative order of ˛, and can be characterized as the smallest positive integer k such that ˛ k D 1: If ˛ D Œa with a 2 Z (and gcd.a; n/ D 1, since ˛ 2 Zn ), then k is also called the multiplicative order of a modulo n, and can be characterized as the smallest positive integer k such that ak 1 .mod n/: From the above discussion, we see that the first k powers of ˛, that is, are distinct. Moreover, other powers of ˛ simply repeat this pattern. The following is an immediate consequence of this observation. ˛0; ˛1; : : : ; ˛k 1,
Theorem 2.12. Let n be a positive integer, and let ˛ be an element of Zn of multiplicative order k. Then for every i 2 Z, we have ˛ i D 1 if and only if k divides i . More generally, for all i; j 2 Z, we have ˛ i D ˛ j if and only if i j .mod k/. Example 2.8. Let n D 7. For each value a D 1; : : : ; 6, we can compute successive powers of a modulo n to find its multiplicative order modulo n.
2.7 Euler’s theorem and Fermat’s little theorem
1i 2i 3i 4i 5i 6i
i mod 7 mod 7 mod 7 mod 7 mod 7 mod 7
1 1 2 3 4 5 6
2 1 4 2 2 4 1
3 1 1 6 1 6 6
4 1 2 4 4 2 1
5 1 4 5 2 3 6
33
6 1 1 1 1 1 1
So we conclude that modulo 7: 1 has order 1; 6 has order 2; 2 and 4 have order 3; and 3 and 5 have order 6. Theorem 2.13 (Euler’s theorem). Let n be a positive integer and ˛ 2 Zn . Then ˛ .n/ D 1. In particular, the multiplicative order of ˛ divides .n/. Proof. Since ˛ 2 Zn , for every ˇ 2 Zn we have ˛ˇ 2 Zn , and so we may define the “multiplication by ˛” map ˛ W Zn ! Zn ˇ 7! ˛ˇ: It is easy to see that ˛ is a bijection: Injectivity: If ˛ˇ D ˛ˇ 0 , then cancel ˛ to obtain ˇ D ˇ 0 . 1
Surjectivity: For every 2 Zn , ˛
is a pre-image of under ˛ .
Zn ,
Thus, as ˇ ranges over the set so does ˛ˇ, and we have Y Y Y .n/ ˇD .˛ˇ/ D ˛ ˇ : ˇ 2Z n
Canceling the common factor (2.8), we obtain
ˇ 2Z n
Q
ˇ 2Z n
(2.8)
ˇ 2Z n
ˇ 2 Zn from the left- and right-hand side of
1 D ˛ .n/ : That proves the first statement of the theorem. The second follows immediately from Theorem 2.12. As a consequence of this, we obtain: Theorem 2.14 (Fermat’s little theorem). For every prime p, and every ˛ 2 Zp , we have ˛ p D ˛. Proof. If ˛ D 0, the statement is obviously true. Otherwise, ˛ 2 Zp , and by Theorem 2.13 we have ˛ p 1 D 1. Multiplying this equation by ˛ yields ˛ p D ˛.
34
Congruences
In the language of congruences, Fermat’s little theorem says that for every prime p and every integer a, we have ap a .mod p/: For a given positive integer n, we say that a 2 Z with gcd.a; n/ D 1 is a primitive root modulo n if the multiplicative order of a modulo n is equal to .n/. If this is the case, then for ˛ WD Œa 2 Zn , the powers ˛ i range over all elements of Zn as i ranges over the interval 0; : : : ; .n/ 1. Not all positive integers have primitive roots — we will see in §7.5 that the only positive integers n for which there exists a primitive root modulo n are n D 1; 2; 4; p e ; 2p e ; where p is an odd prime and e is a positive integer. The following theorem is sometimes useful in determining the multiplicative order of an element in Zn . Theorem 2.15. Suppose ˛ 2 Zn has multiplicative order k. Then for every m 2 Z, the multiplicative order of ˛ m is k= gcd.m; k/. Proof. Applying Theorem 2.12 to ˛ m , we see that the multiplicative order of ˛ m is the smallest positive integer ` such that ˛ m` D 1. But we have ˛ m` D 1 ” m` 0 .mod k/ (applying Theorem 2.12 to ˛) ” ` 0 .mod k= gcd.m; k// (by Theorem 2.5): E XERCISE 2.26. Find all elements of Z19 of multiplicative order 18. E XERCISE 2.27. Let n 2 Z with n > 1. Show that n is prime if and only if ˛ n 1 D 1 for every non-zero ˛ 2 Zn . E XERCISE 2.28. Let n D pq where p and q are distinct primes, and let m WD lcm.p 1; q 1/. Show that ˛ m D 1 for all ˛ 2 Zn . E XERCISE 2.29. Let p be any prime other than 2 or 5. Show that p divides infinitely many of the numbers 9, 99, 999, etc. E XERCISE 2.30. Let n be an integer greater than 1. Show that n does not divide 2n 1. E XERCISE 2.31. Prove the following generalization of Fermat’s little theorem: for every positive integer n, and every ˛ 2 Zn , we have ˛ n D ˛ n .n/ .
2.8 Quadratic residues
35
E XERCISE 2.32. This exercise develops an alternative proof of Fermat’s little theorem. (a) Using Exercise 1.14, show that for all primes p and integers a, we have .a C 1/p ap C 1 .mod p/. (b) Now derive Fermat’s little theorem from part (a). 2.8 Quadratic residues In §2.3, we studied linear congruences. It is natural to study congruences of higher degree as well. In this section, we study a special case of this more general problem, namely, congruences of the form z 2 a .mod n/. The theory we develop here nicely illustrates many of the ideas we have discussed earlier, and has a number of interesting applications as well. We begin with some general, preliminary definitions and general observations about powers in Zn . For each integer m, we define .Zn /m WD fˇ m W ˇ 2 Zn g; the set of mth powers in Zn . The set .Zn /m is non-empty, as it obviously contains Œ1. Theorem 2.16. Let n be a positive integer, let ˛; ˇ 2 Zn , and let m be any integer. (i) If ˛ 2 .Zn /m , then ˛
1
2 .Zn /m .
(ii) If ˛ 2 .Zn /m and ˇ 2 .Zn /m , then ˛ˇ 2 .Zn /m . (iii) If ˛ 2 .Zn /m and ˇ … .Zn /m , then ˛ˇ … .Zn /m . Proof. For (i), if ˛ D m , then ˛ 1 D . 1 /m . For (ii), if ˛ D m and ˇ D ı m , then ˛ˇ D . ı/m . (iii) follows from (i) and (ii). Suppose that ˛ 2 .Zn /m , ˇ … .Zn /m , and ˛ˇ 2 .Zn /m . Then by (i), ˛ 1 2 .Zn /m , and by (ii), ˇ D ˛ 1 .˛ˇ/ 2 .Zn /m , a contradiction. Theorem 2.17. Let n be a positive integer. For each ˛ 2 Zn , and all `; m 2 Z with gcd.`; m/ D 1, if ˛ ` 2 .Zn /m , then ˛ 2 .Zn /m . Proof. Suppose ˛ ` D ˇ m 2 .Zn /m . Since gcd.`; m/ D 1, there exist integers s and t such that `s C mt D 1. We then have ˛ D ˛ `sCmt D ˛ `s ˛ mt D ˇ ms ˛ mt D .ˇ s ˛ t /m 2 .Zn /m : We now focus on the on squares in Zn , rather than general powers. An integer a is called a quadratic residue modulo n if gcd.a; n/ D 1 and a b 2 .mod n/ for
36
Congruences
some integer b; in this case, we say that b is a square root of a modulo n. In terms of residue classes, a is a quadratic residue modulo n if and only if Œa 2 .Zn /2 . To avoid some annoying technicalities, from now on, we shall consider only the case where n is odd. 2.8.1 Quadratic residues modulo p We first study quadratic residues modulo an odd prime p, and we begin by determining the square roots of 1 modulo p. Theorem 2.18. Let p be an odd prime and ˇ 2 Zp . Then ˇ 2 D 1 if and only if ˇ D ˙1. Proof. Clearly, if ˇ D ˙1, then ˇ 2 D 1. Conversely, suppose that ˇ 2 D 1. Write ˇ D Œb, where b 2 Z. Then we have b 2 1 .mod p/, which means that p j .b 2
1/ D .b
1/.b C 1/;
and since p is prime, we must have p j .b 1/ or p j .b C 1/. This implies b ˙1 .mod p/, or equivalently, ˇ D ˙1. This theorem says that modulo p, the only square roots of 1 are 1 and 1, which obviously belong to distinct residue classes (since p > 2). From this seemingly trivial fact, a number of quite interesting and useful results may be derived. Theorem 2.19. Let p be an odd prime and ; ˇ 2 Zp . Then 2 D ˇ 2 if and only if D ˙ˇ. Proof. This follows from the previous theorem:
2 D ˇ 2 ” . =ˇ/2 D 1 ” =ˇ D ˙1 ” D ˙ˇ: This theorem says that if ˛ D ˇ 2 for some ˇ 2 Zp , then ˛ has precisely two square roots: ˇ and ˇ. Theorem 2.20. Let p be an odd prime. Then j.Zp /2 j D .p
1/=2.
Proof. By the previous theorem, the “squaring map” W Zp ! Zp that sends ˇ to ˇ 2 is a two-to-one map: every element in the image of has precisely two pre-images. As a general principle, if we have a function f W A ! B, where A is a finite set and every element in f .A/ has exactly d pre-images, then jf .A/j D jAj=d . Applying this general principle to our setting, we see that the image of is half the size of Zp . Thus, for every odd prime p, exactly half the elements of Zp are squares, and
37
2.8 Quadratic residues
half are non-squares. If we choose our representatives for the residue classes modulo p from the interval Πp=2; p=2/, we may list the elements of Zp as Π.p
1/=2; : : : ; Œ 1; Œ0; Œ1; : : : ; Œ.p
1/=2:
We then see that Zp consists of the residue classes Œ˙1; : : : ; Œ˙.p
1/=2;
and so .Zp /2 consists of the residue classes Œ12 ; : : : ; Œ.p
1/=22 ;
which must be distinct, since we know that j.Zp /2 j D .p
1/=2.
Example 2.9. Let p D 7. We can list the elements of Zp as Œ˙1; Œ˙2; Œ˙3: Squaring these, we see that .Zp /2 D fŒ12 ; Œ22 ; Œ32 g D fŒ1; Œ4; Œ2g: We next derive an extremely important characterization of quadratic residues. Theorem 2.21 (Euler’s criterion). Let p be an odd prime and ˛ 2 Zp . (i) ˛ .p
1/=2
D ˙1.
(ii) If ˛ 2 .Zp /2 then ˛ .p (iii) If ˛ …
.Zp /2
then
1/=2
˛ .p 1/=2
Proof. For (i), let D ˛ .p
1/=2 .
D 1. D
1.
By Euler’s theorem (Theorem 2.13), we have
2 D ˛p
1
D 1;
and hence by Theorem 2.18, we have D ˙1. For (ii), suppose that ˛ D ˇ 2 . Then again by Euler’s theorem, we have ˛ .p
1/=2
D .ˇ 2 /.p
1/=2
D ˇp
1
D 1:
For (iii), let ˛ 2 Zp n .Zp /2 . We study the product Y WD ˇ: ˇ 2Zp
We shall show that, on the one hand, D ˛ .p 1/=2 , while on the other hand, D 1. To show that D ˛ .p 1/=2 , we group elements of Zp into pairs of distinct elements whose product is ˛. More precisely, let P WD fS Zp W jS j D 2g, and
38
Congruences
define C WD f f; g 2 P W D ˛g. Note that for every 2 Zp , there is a unique 2 Zp such that D ˛, namely, WD ˛=; moreover, ¤ , since otherwise, we would have 2 D ˛, contradicting the assumption that ˛ … .Zp /2 . Thus, every element of Zp belongs to exactly one pair in C; in other words, the elements of C form a partition of Zp . It follows that Y Y . / D ˛ D ˛ .p 1/=2 : D f;g2C
f;g2C
To show that D 1, we group elements of Zp into pairs of distinct elements whose product is Œ1. Define D WD f f; g 2 P W D 1g. For every 2 Zp , there exists a unique 2 Zp such that D 1, namely, WD 1 ; moreover, D if and only if 2 D 1, and by Theorem 2.18, this happens if and only if D ˙1. Thus, every element of Zp except for Œ˙1 belongs to exactly one pair in D; in other words, the elements of D form a partition of Zp n fŒ˙1g. It follows that Y Y D Œ1 Œ 1 . / D Œ 1 Œ1 D 1: f;g2D
f;g2D
Thus, Euler’s criterion says that for every ˛ 2 Zp , we have ˛ .p ˛ 2 .Zp /2 ” ˛ .p
1/=2
1/=2
D ˙1 and
D 1:
In the course of proving Euler’s criterion, we proved the following result, which we state here for completeness: Q Theorem 2.22 (Wilson’s theorem). Let p be an odd prime. Then ˇ 2Zp ˇ D 1. In the language of congruences, Wilson’s theorem may be stated as follows: 1/Š
.p
1 .mod p/:
We also derive the following simple consequence of Theorem 2.21: Theorem 2.23. Let p be an odd prime and ˛; ˇ 2 Zp . If ˛ … .Zp /2 and ˇ … .Zp /2 , then ˛ˇ 2 .Zp /2 . Proof. Suppose ˛ … .Zp /2 and ˇ … .Zp /2 . Then by Euler’s criterion, we have ˛ .p
1/=2
D
1 and ˇ .p
1/=2
D
1:
Therefore, .˛ˇ/.p
1/=2
D ˛ .p
1/=2
ˇ .p
1/=2
D Π1 Π1 D 1;
which again by Euler’s criterion implies that ˛ˇ 2 .Zp /2 .
2.8 Quadratic residues
39
This theorem, together with parts (ii) and (iii) of Theorem 2.16, gives us the following simple rules regarding squares in Zp : square square D square; square non-square D non-square; non-square non-square D square: 2.8.2 Quadratic residues modulo p e We next study quadratic residues modulo p e , where p is an odd prime. The key is to establish the analog of Theorem 2.18: Theorem 2.24. Let p be an odd prime, e be a positive integer, and ˇ 2 Zpe . Then ˇ 2 D 1 if and only if ˇ D ˙1. Proof. Clearly, if ˇ D ˙1, then ˇ 2 D 1. Conversely, suppose that ˇ 2 D 1. Write ˇ D Œb, where b 2 Z. Then we have b 2 1 .mod p e /, which means that p e j .b 2
1/ D .b
1/.b C 1/:
In particular, p j .b 1/.b C 1/, and so p j .b 1/ or p j .b C 1/. Moreover, p cannot divide both b 1 and b C 1, as otherwise, it would divide their difference .b C 1/ .b 1/ D 2, which is impossible (because p is odd). It follows that p e j .b 1/ or p e j .b C 1/, which means ˇ D ˙1. Theorems 2.19–2.23 generalize immediately from Zp to Zpe : we really used nothing in the proofs of these theorems other than the fact that ˙1 are the only square roots of 1 modulo p. As such, we state the analogs of these theorems for Zpe without proof. Theorem 2.25. Let p be an odd prime, e be a positive integer, and ; ˇ 2 Zpe . Then 2 D ˇ 2 if and only if D ˙ˇ. Theorem 2.26. Let p be an odd prime and e be a positive integer. Then j.Zpe /2 j D .p e /=2. Theorem 2.27. Let p be an odd prime, e be a positive integer, and ˛ 2 Zpe . (i) ˛ .p
e /=2
D ˙1.
(ii) If ˛ 2 .Zpe /2 then ˛ .p (iii) If ˛ …
.Zpe /2
then
e /=2
e ˛ .p /=2
D 1. D
1.
Theorem 2.28. Let p be an odd prime and e be a positive integer. Q ˇ 2Ze ˇ D 1. p
Then
40
Congruences
Theorem 2.29. Let p be an odd prime, e be a positive integer, and ˛; ˇ 2 Zpe . If ˛ … .Zpe /2 and ˇ … .Zpe /2 , then ˛ˇ 2 .Zpe /2 . It turns out that an integer is a quadratic residue modulo p e if and only if it is a quadratic residue modulo p. Theorem 2.30. Let p be an odd prime, e be a positive integer, and a be any integer. Then a is a quadratic residue modulo p e if and only if a is a quadratic residue modulo p. Proof. Suppose that a is a quadratic residue modulo p e . Then a is not divisible by p and a b 2 .mod p e / for some integer b. It follows that a b 2 .mod p/, and so a is a quadratic residue modulo p. Suppose that a is not a quadratic residue modulo p e . If a is divisible by p, then by definition a is not a quadratic residue modulo p. So suppose a is not divisible by p. By Theorem 2.27, we have ap
e 1 .p
1/=2
1 .mod p e /:
This congruence holds modulo p as well, and by Fermat’s little theorem (applied e 1 times), 2
a ap ap ap
e 1
.mod p/;
and so 1 ap
e 1 .p
1/=2
a.p
1/=2
.mod p/:
Theorem 2.21 therefore implies that a is not a quadratic residue modulo p. 2.8.3 Quadratic residues modulo n We now study quadratic residues modulo n, where n is an arbitrary, odd integer, with n > 1. Let n D p1e1 prer be the prime factorization of n. Our main tools here are the Chinese remainder map W Zn ! Zpe1 Zprer ; 1
introduced in Theorem 2.8, together with the results developed so far for quadratic residues modulo odd prime powers. Let ˛ 2 Zn with .˛/ D .˛1 ; : : : ; ˛r /.
41
2.8 Quadratic residues
On the one hand, suppose that ˛ D ˇ 2 for some ˇ 2 Zn . If .ˇ/ D .ˇ1 ; : : : ; ˇr /, we have .˛1 ; : : : ; ˛r / D .˛/ D .ˇ 2 / D .ˇ12 ; : : : ; ˇr2 /; where we have used part (iii.c) of Theorem 2.8. It follows that ˛i D ˇi2 for each i . On the other hand, suppose that for each i , ˛i D ˇi2 for some ˇi 2 Zp ei . i Then setting ˇ WD 1 .ˇ1 ; : : : ; ˇr /, we have .ˇ 2 / D .ˇ12 ; : : : ; ˇr2 / D .˛1 ; : : : ; ˛r / D .˛/; where we have again used part (iii.c) of Theorem 2.8, along with the fact that is bijective (to define ˇ). Thus, .˛/ D .ˇ 2 /, and again since is bijective, it follows that ˛ D ˇ 2 . We have shown that 2 ˛ 2 .Zn /2 ” ˛i 2 Zei for i D 1; : : : ; r: pi
In particular, restricting to .Zn /2 yields a one-to-one correspondence between .Zn /2 and 2 2 Ze1 Zpr er ; p1
and therefore, by Theorem 2.26 (and Theorem 2.9), we have j.Zn /2 j
D
r Y
. .piei /=2/ D .n/=2r :
i D1
Now suppose that ˛ D ˇ 2 , with ˇ 2 Zn and .ˇ/ D .ˇ1 ; : : : ; ˇr /. Consider an arbitrary element 2 Zn , with . / D . 1 ; : : : ; r /. Then we have
2 D ˇ 2 ” . 2 / D .ˇ 2 / ” . 12 ; : : : ; r2 / D .ˇ12 ; : : : ; ˇr2 / ” . 1 ; : : : ; r / D .˙ˇ1 ; : : : ; ˙ˇr / (by Theorem 2.25): Therefore, ˛ has precisely 2r square roots, namely,
1 .˙ˇ ; : : : ; ˙ˇ /: 1 r
2.8.4 Square roots of 1 modulo p Using Euler’s criterion, we can easily characterize those primes modulo which 1 is a quadratic residue. This turns out to have a number of nice applications. Consider an odd prime p. The following theorem says that the question of whether 1 is a quadratic residue modulo p is decided by the residue class of p modulo 4. Since p is odd, either p 1 .mod 4/ or p 3 .mod 4/.
42
Congruences
Theorem 2.31. Let p be an odd prime. Then 1 is a quadratic residue modulo p if and only p 1 .mod 4/. Proof. By Euler’s criterion, 1 is a quadratic residue modulo p if and only if . 1/.p 1/=2 1 .mod p/. If p 1 .mod 4/, then .p 1/=2 is even, and so . 1/.p 1/=2 D 1. If p 3 .mod 4/, then .p 1/=2 is odd, and so . 1/.p 1/=2 D 1. In fact, when p 1 .mod 4/, any non-square in Zp yields a square root of 1 modulo p, as follows: Theorem 2.32. Let p be a prime with p 1 .mod 4/, 2 Zp n .Zp /2 , and ˇ WD .p 1/=4 . Then ˇ 2 D 1. Proof. This is a simple calculation, based on Euler’s criterion: ˇ 2 D .p
1/=2
D
1:
The fact that 1 is a quadratic residue modulo primes p 1 .mod 4/ can be used to prove Fermat’s theorem that such primes may be written as the sum of two squares. To do this, we first need the following technical lemma: Theorem 2.33 (Thue’s lemma). Let n; b; r ; t 2 Z, with 0 < r n < r t . Then there exist r; t 2 Z with r bt .mod n/; jrj < r ; and 0 < jtj < t : Proof. For i D 0; : : : ; r 1 and j D 0; : : : ; t 1, we define the number vij WD i bj . Since we have defined r t numbers, and r t > n, two of these numbers must lie in the same residue class modulo n; that is, for some .i1 ; j1 / ¤ .i2 ; j2 /, we have vi1 j1 vi2 j2 .mod n/. Setting r WD i1 i2 and t WD j1 j2 , this implies r bt .mod n/, jrj < r , jtj < t , and that either r ¤ 0 or t ¤ 0. It only remains to show that t ¤ 0. Suppose to the contrary that t D 0. This would imply that r 0 .mod n/ and r ¤ 0, which is to say that r is a non-zero multiple of n; however, this is impossible, since jrj < r n. Theorem 2.34 (Fermat’s two squares theorem). Let p be an odd prime. Then p D r 2 C t 2 for some r; t 2 Z if and only if p 1 .mod 4/. Proof. One direction is easy. Suppose p 3 .mod 4/. It is easy to see that the square of every integer is congruent to either 0 or 1 modulo 4; therefore, the sum of two squares is congruent to either 0, 1, or 2 modulo 4, and so can not be congruent to p modulo 4 (let alone equal to p). For the other direction, suppose p 1 .mod 4/. We know that 1 is a quadratic residue modulo p, so let b be an integer such that b 2 1 .mod p/. Now
43
2.8 Quadratic residues
p
apply Theorem 2.33 with n WD p, b as just defined, and r WD t WD b pc C 1. p p p Evidently, b pc C 1 > p, and hence r t > p. Also, since p is prime, p is p p p not an integer, and so b pc < p < p; in particular, r D b pc C 1 p. Thus, the hypotheses of that theorem are satisfied, and therefore, there exist integers r and t such that p p p p r bt .mod p/; jrj b pc < p; and 0 < jtj b pc < p: It follows that r 2 b2t 2
t 2 .mod p/:
Thus, r 2 C t 2 is a multiple of p and 0 < r 2 C t 2 < 2p. The only possibility is that r 2 C t 2 D p. The fact that 1 is a quadratic residue modulo an odd prime p only if p 1 .mod 4/ can be used so show there are infinitely many such primes. Theorem 2.35. There are infinitely many primes p 1 .mod 4/. Proof. Suppose there were only finitely many such primes, p1 ; : : : ; pk . Set M WD Qk 2 i D1 pi and N WD 4M C 1. Let p be any prime dividing N . Evidently, p is not among the pi ’s, since if it were, it would divide both N and 4M 2 , and so also N 4M 2 D 1. Also, p is clearly odd, since N is odd. Moreover, .2M /2 1 .mod p/; therefore, 1 is a quadratic residue modulo p, and so p 1 .mod 4/, contradicting the assumption that p1 ; : : : ; pk are the only such primes. For completeness, we also state the following fact: Theorem 2.36. There are infinitely many primes p 3 .mod 4/. Proof. Suppose there were only finitely many such primes, p1 ; : : : ; pk . Set M WD Qk 1. Since N 3 .mod 4/, there must be some prime i D1 pi and N WD 4M p 3 .mod 4/ dividing N (if all primes dividing N were congruent to 1 modulo 4, then so too would be their product N ). Evidently, p is not among the pi ’s, since if it were, it would divide both N and 4M , and so also 4M N D 1. This contradicts the assumption that p1 ; : : : ; pk are the only primes congruent to 3 modulo 4. E XERCISE 2.33. Let n be a positive integer, let m be an integer, and let d WD gcd.m; .n//. Show that: (a) if d D 1, then .Zn /m D .Zn /; (b) if ˛ 2 .Zn /m , then ˛ .n/=d D 1. E XERCISE 2.34. Calculate the sets C and D in the proof of Theorem 2.21 in the case p D 11 and ˛ D 1.
44
Congruences
E XERCISE 2.35. Calculate the square roots of 1 modulo 4, 8, and 16. E XERCISE 2.36. Let n 2 Z with n > 1. Show that n is prime if and only if .n 1/Š 1 .mod n/. E XERCISE 2.37. Let p be a prime with p 1 .mod 4/, and b WD ..p Show that b 2 1 .mod p/.
1/=2/Š.
E XERCISE 2.38. Let n WD pq, where p and q are distinct, odd primes. Show that there exist ˛; ˇ 2 Zn such that ˛ … .Zn /2 , ˇ … .Zn /2 , and ˛ˇ … .Zn /2 . E XERCISE 2.39. Let n be an odd positive integer, and let a be any integer. Show that a is a quadratic residue modulo n if and only if a is a quadratic residue modulo p for each prime p j n. E XERCISE 2.40. Show that if p is an odd prime, with p 3 .mod 4/, then .Zp /4 D .Zp /2 . More generally, show that if n is an odd positive integer, where p 3 .mod 4/ for each prime p j n, then .Zn /4 D .Zn /2 . E XERCISE 2.41. Let p be an odd prime, and let e 2 Z with e > 1. Let a be an integer of the form a D p f b, where 0 f < e and p − b. Consider the integer solutions z to the congruence z 2 a .mod p e /. Show that a solution exists if and only if f is even and b is a quadratic residue modulo p, in which case there are exactly 2p f distinct solutions modulo p e . E XERCISE 2.42. Suppose p is an odd prime, and that r 2 C t 2 D p for some integers r; t. Show that if x; y are integers such that x 2 C y 2 D p, then .x; y/ 2 f.˙r; ˙t/; .˙t; ˙r/g. E XERCISE 2.43. Show that if both u and v are the sum of two squares of integers, then so is their product uv. E XERCISE 2.44. Suppose r 2 C t 2 0 .mod n/, where n is a positive integer, and suppose p is an odd prime dividing n. Show that: (a) if p divides neither r nor t , then p 1 .mod 4/; (b) if p divides one of r or t , then it divides the other, and moreover, p 2 divides n, and .r=p/2 C .t =p/2 0 .mod n=p 2 /. E XERCISE 2.45. Let n be a positive integer, and write n D ab 2 where a and b are positive integers, and a is square-free (see Exercise 1.15). Show that n is the sum of two squares of integers if and only if no prime p 3 .mod 4/ divides a. Hint: use the previous two exercises.
45
2.9 Summations over divisors
2.9 Summations over divisors We close this chapter with a brief treatment of summations over divisors. To this end, we introduce some terminology and notation. By an arithmetic function, we simply mean a function from the positive integers into the reals (actually, one usually considers complex-valued functions as well, but we shall not do so here). Let f and g be arithmetic functions. The Dirichlet product of f and g, denoted f ? g, is the arithmetic function whose value at n is defined by the formula X .f ? g/.n/ WD f .d /g.n=d /; d jn
the sum being over all positive divisors d of n. Another, more symmetric, way to write this is X .f ? g/.n/ D f .d1 /g.d2 /; nDd1 d2
the sum being over all pairs .d1 ; d2 / of positive integers with d1 d2 D n. The Dirichlet product is clearly commutative (i.e., f ? g D g ? f ), and is associative as well, which one can see by checking that X .f ? .g ? h//.n/ D f .d1 /g.d2 /h.d3 / D ..f ? g/ ? h/.n/; nDd1 d2 d3
the sum being over all triples .d1 ; d2 ; d3 / of positive integers with d1 d2 d3 D n. We now introduce three special arithmetic functions: ı, 1, and . The functions ı and 1 are defined as follows: 1 if n D 1; ı.n/ WD 1.n/ WD 1: 0 if n > 1; The Möbius function is defined as follows: if n D p1e1 prer is the prime factorization of n, then 0 if ei > 1 for some i D 1; : : : ; rI .n/ WD r . 1/ otherwise: In other words, .n/ D 0 if n is not square-free (see Exercise 1.15); otherwise, .n/ is . 1/r where r is the number of distinct primes dividing n. Here are some examples: .1/ D 1; .2/ D
1; .3/ D
1; .4/ D 0; .5/ D
1; .6/ D 1:
It is easy to see from the definitions that for every arithmetic function f , we have X ı ? f D f and .1 ? f /.n/ D f .d /: d jn
46
Congruences
Thus, ı acts as a multiplicative identity with respect to the Dirichlet product, while “1 ? ” acts as a “summation over divisors” operator. An arithmetic function f is called multiplicative if f .1/ D 1 and for all positive integers n; m with gcd.n; m/ D 1, we have f .nm/ D f .n/f .m/. The reader may easily verify that ı, 1, and are multiplicative functions. Theorem 2.9 says that Euler’s function is multiplicative. The reader may also verify the following: Theorem 2.37. If f is a multiplicative arithmetic function, and if n D p1e1 prer is the prime factorization of n, then f .n/ D f .p1e1 / f .prer /: Proof. Exercise. A key property of the Möbius function is the following: Theorem 2.38. Let f be a multiplicative arithmetic function. If n D p1e1 prer is the prime factorization of n, then X .d /f .d / D .1 f .p1 // .1 f .pr //: (2.9) d jn
Proof. The only non-zero terms appearing in the sum on the left-hand side of (2.9) are those corresponding to divisors d of the form pi1 pi` , where pi1 ; : : : ; pi` are distinct; the value contributed to the sum by such a term is . 1/` f .pi1 pi` / D . 1/` f .pi1 / f .pi` /. These are the same as the terms in the expansion of the product on the right-hand side of (2.9). If we set f WD 1 in the previous theorem, then we see that X 1 if n D 1; .d / D 0 if n > 1. d jn
Translating this into the language of Dirichlet products, we have 1 ? D ı: Thus, with respect to the Dirichlet product, the functions 1 and are multiplicative inverses of one another. Based on this, we may easily derive the following: Theorem 2.39 (Möbius inversion formula). Let f and F be arithmetic functions. Then F D 1 ? f if and only if f D ? F . Proof. If F D 1 ? f , then ? F D ? .1 ? f / D . ? 1/ ? f D ı ? f D f;
2.9 Summations over divisors
47
and conversely, if f D ? F , then 1 ? f D 1 ? . ? F / D .1 ? / ? F D ı ? F D F: The Möbius inversion formula says this: X F .n/ D f .d / for all positive integers n d jn
” f .n/ D
X
.d /F .n=d / for all positive integers n.
d jn
The Möbius inversion formula is a useful tool. As an application, we use it to obtain a simple proof of the following fact: P Theorem 2.40. For every positive integer n, we have d jn .d / D n: Proof. Let us define the arithmetic functions N.n/ WD n and M.n/ WD 1=n. Our goal is to show that N D 1 ? , and by Möbius inversion, it suffices to show that ? N D . If n D p1e1 prer is the prime factorization of n, we have X X . ? N /.n/ D .d /.n=d / D n .d /=d d jn r Y
Dn
d jn
.1
1=pi / (applying Theorem 2.38 with f WD M )
i D1
D .n/ (by Theorem 2.11): E XERCISE 2.46. In our definition of a multiplicative function f , we made the requirement that f .1/ D 1. Show that if we dropped this requirement, the only other function that would satisfy the definition would be the zero function (i.e., the function that is everywhere zero). E XERCISE 2.47. Let f be a polynomial with integer coefficients, and for every positive integer n, define !f .n/ to be the number of integers x 2 f0; : : : ; n 1g such that f .x/ 0 .mod n/. Show that !f is multiplicative. E XERCISE 2.48. Show that if f and g are multiplicative, then so is f ? g. Hint: use Exercise 1.18. E XERCISE 2.49. Let .n/ be the number of positive divisors of n. Show that: (a) is a multiplicative function; Q (b) .n/ D riD1 .ei C 1/; where n D p1e1 prer is the prime factorization of n;
48
Congruences
(c)
P
(d)
P
d jn .d / .n=d / d jn .d / .d /
D 1;
D . 1/r ; where n D p1e1 prer is the prime factorization
of n. E XERCISE 2.50. Define .n/ WD
P
d jn d .
Show that:
(a) is a multiplicative function; Q (b) .n/ D riD1 .piei C1 1/=.pi 1/; where n D p1e1 prer is the prime factorization of n; P (c) d jn .d /.n=d / D n; P e1 er r (d) d jn .d /.d / D . 1/ p1 pr ; where n D p1 pr is the prime factorization of n. E XERCISE 2.51. The Mangoldt function ƒ.n/ is defined for all positive integers n as follows: ƒ.n/ WD log p, if n D p k for some prime p and positive integer k, P and ƒ.n/ WD 0, otherwise. Show that d jn ƒ.d / D log n, and from this, deduce P that ƒ.n/ D d jn .d / log d . E XERCISE 2.52. Show that if f is multiplicative, and if n D p1e1 prer is the P prime factorization of n, then d jn .d /2 f .d / D .1 C f .p1 // .1 C f .pr //: P E XERCISE 2.53. Show that n is square-free if and only if d jn .d /2 .d / D n. E XERCISE 2.54. Show that for every arithmetic function f with f .1/ ¤ 0, there is a unique arithmetic function g, called the Dirichlet inverse of f , such that f ? g D ı. Also, show that if f .1/ D 0, then f has no Dirichlet inverse. E XERCISE 2.55. Show that if f is a multiplicative function, then so is its Dirichlet inverse (as defined in the previous exercise). E XERCISE 2.56. This exercise develops an alternative proof of Theorem 2.40 that does not depend on Theorem 2.11. Let n be a positive integer. Define Fn WD fi=n 2 Q W i D 0; : : : ; n 1g. Also, for each positive integer d , define Gd WD fa=d 2 Q W a 2 Z; gcd.a; d / D 1g. (a) Show that for each x 2 Fn , there exists a unique positive divisor d of n such that x 2 Gd . (b) Show that for each positive divisor d of n, we have Fn \ Gd D fa=d W a 2 f0; : : : ; d 1g; gcd.a; d / D 1g. P (c) Using (a) and (b), show that d jn .d / D n. E XERCISE 2.57. Using Möbius inversion, directly derive Theorem 2.11 from Theorem 2.40.
3 Computing with large integers
In this chapter, we review standard asymptotic notation, introduce the formal computational model we shall use throughout the rest of the text, and discuss basic algorithms for computing with large integers. 3.1 Asymptotic notation We review some standard notation for relating the rate of growth of functions. This notation will be useful in discussing the running times of algorithms, and in a number of other contexts as well. Let f and g be real-valued functions, both defined either on the set of nonnegative integers, or on the set of non-negative reals. Actually, as we are only concerned about the behavior of f .x/ and g.x/ as x ! 1, we only require that f .x/ and g.x/ are defined for all sufficiently large x. We further assume that g is eventually positive, meaning that g.x/ > 0 for all sufficiently large x. Then f D O.g/ means that jf .x/j cg.x/ for some positive constant c and all sufficiently large x (read, “f is big-O of g”), f D .g/ means that f .x/ cg.x/ for some positive constant c and all sufficiently large x (read, “f is big-Omega of g”), f D ‚.g/ means that cg.x/ f .x/ dg.x/ for some positive constants c and d and all sufficiently large x (read, “f is big-Theta of g”), f D o.g/ means that f .x/=g.x/ ! 0 as x ! 1 (read, “f is little-o of g”), and f g means that f .x/=g.x/ ! 1 as x ! 1 (read, “f is asymptotically equal to g”). Example 3.1. Let f .x/ WD x 2 and g.x/ WD 2x 2 f D .g/. Indeed, f D ‚.g/. 49
10x C 1. Then f D O.g/ and
50
Computing with large integers
Example 3.2. Let f .x/ WD x 2 and g.x/ WD x 2
10x C 1. Then f g.
Example 3.3. Let f .x/ WD 100x 2 and g.x/ WD x 3 . Then f D o.g/. Note that by definition, if we write f D .g/, f D ‚.g/, or f g, it must be the case that f (in addition to g) is eventually positive; however, if we write f D O.g/ or f D o.g/, then f need not be eventually positive. When one writes “f D O.g/,” one should interpret “ D O./” as a binary relation between f with g. Analogously for “f D .g/,” “f D ‚.g/,” and “f D o.g/.” One may also write “O.g/” in an expression to denote an anonymous function f such that f D O.g/. Analogously, .g/, ‚.g/, and o.g/ may denote anonymous functions. The expression O.1/ denotes a function bounded in absolute value by a constant, while the expression o.1/ denotes a function that tends to zero in the limit. Example 3.4. Let f .x/ WD x 3 2x 2 Cx 3. One could write f .x/ D x 3 CO.x 2 /: Here, the anonymous function is g.x/ WD 2x 2 Cx 3, and clearly g.x/ D O.x 2 /. One could also write f .x/ D x 3 .2 C o.1//x 2 : Here, the anonymous function is g.x/ WD 1=x C 3=x 2 . While g D o.1/, it is only defined for x > 0. This is acceptable, as we will only regard statements such as this asymptotically, as x ! 1. As an even further use (abuse?) of the notation, one may use the big-O, -Omega, and -Theta notation for functions on an arbitrary domain, in which case the relevant inequalities should hold throughout the entire domain. This usage includes functions of several independent variables, as well as functions defined on sets with no natural ordering. E XERCISE 3.1. Show that: (a) f D o.g/ implies f D O.g/ and g ¤ O.f /; (b) f D O.g/ and g D O.h/ implies f D O.h/; (c) f D O.g/ and g D o.h/ implies f D o.h/; (d) f D o.g/ and g D O.h/ implies f D o.h/. E XERCISE 3.2. Let f and g be eventually positive functions. Show that: (a) f g if and only if f D .1 C o.1//g; (b) f g implies f D ‚.g/; (c) f D ‚.g/ if and only if f D O.g/ and f D .g/; (d) f D .g/ if and only if g D O.f /.
51
3.1 Asymptotic notation
E XERCISE 3.3. Suppose f1 D O.g1 / and f2 D O.g2 /. Show that f1 C f2 D O.max.g1 ; g2 //, f1 f2 D O.g1 g2 /, and that for every positive constant c, cf1 D O.g1 /. E XERCISE 3.4. Suppose that f .x/ c C dg.x/ for some positive constants c and d , and for all sufficiently large x. Show that if g D .1/, then f D O.g/. E XERCISE 3.5. Suppose f and g are defined on the integers i k, and that g.i / > 0 for all i k. Show that if f D O.g/, then there exists a positive constant c such that jf .i /j cg.i / for all i k. E XERCISE 3.6. Let f and g be eventually positive functions, and suppose f .x/=g.x/ tends to a limit L (possibly L D 1) as x ! 1. Show that: (a) if L D 0, then f D o.g/; (b) if 0 < L < 1, then f D ‚.g/; (c) if L D 1, then g D o.f /. E XERCISE 3.7. Let f .x/ WD x ˛ .log x/ˇ and g.x/ WD x .log x/ı , where ˛; ˇ; ; ı are non-negative constants. Show that if ˛ < , or if ˛ D and ˇ < ı, then f D o.g/. E XERCISE 3.8. Order the following functions in x so that for each adjacent pair f; g in the ordering, we have f D O.g/, and indicate if f D o.g/, f g, or g D O.f /: p x 3 ; e x x 2 ; 1=x; x 2 .x C 100/ C 1=x; x C x; log2 x; log3 x; 2x 2 ; x; e
x
; 2x 2
10x C 4; e xC
p
x
; 2x ; 3x ; x
2
; x 2 .log x/1000 :
E XERCISE 3.9. Show that: (a) the relation “” is an equivalence relation on the set of eventually positive functions; (b) for all eventually positive functions f1 ; f2 ; g1 ; g2 , if f1 g1 and f2 g2 , then f1 ? f2 g1 ? g2 , where “?” denotes addition, multiplication, or division; (c) for all eventually positive functions f; g, and every ˛ > 0, if f g, then f ˛ g˛ ; (d) for all eventually positive functions f; g, and every function h such that h.x/ ! 1 as x ! 1, if f g, then f B h g B h, where “B” denotes function composition. E XERCISE 3.10. Show that all of the claims in the previous exercise also hold when the relation “” is replaced with the relation “ D ‚./.”
52
Computing with large integers
E XERCISE 3.11. Let f; g be eventually positive functions. Show that: (a) f D ‚.g/ if and only if log f D log g C O.1/; (b) f g if and only if log f D log g C o.1/. E XERCISE 3.12. Suppose that f and g are functions defined on the integers k; kC P 1; : : : ; and that g is eventually positive. For n k, define F .n/ WD niDk f .i / P and G.n/ WD niDk g.i /. Show that if f D O.g/ and G is eventually positive, then F D O.G/. E XERCISE 3.13. Suppose that f and g are piece-wise continuous Ron Œa; 1/ (see x §A4), andRthat g is eventually positive. For x a, define F .x/ WD a f .t /dt and x G.x/ WD a g.t/dt . Show that if f D O.g/ and G is eventually positive, then F D O.G/. E XERCISE 3.14. Suppose that f and g are functions defined on the integers k; k C 1; : : : ; both of which are eventually positive. For n k, define F .n/ WD Pn Pn i Dk f .i/ and G.n/ WD i Dk g.i /. Show that if f g and G.n/ ! 1 as n ! 1, then F G. E XERCISE 3.15. Suppose that f and g are piece-wise continuous on Œa; R x1/ (see WD §A4), both of which are eventually positive. For x a, define F .x/ a f .t /dt Rx and G.x/ WD a g.t/dt . Show that if f g and G.x/ ! 1 as x ! 1, then F G. E XERCISE 3.16. Give an example of two non-decreasing functions f and g, both mapping positive integers to positive integers, such that f ¤ O.g/ and g ¤ O.f /. 3.2 Machine models and complexity theory When presenting an algorithm, we shall always use a high-level, and somewhat informal, notation. However, all of our high-level descriptions can be routinely translated into the machine-language of an actual computer. So that our theorems on the running times of algorithms have a precise mathematical meaning, we formally define an “idealized” computer: the random access machine or RAM. A RAM consists of an unbounded sequence of memory cells mŒ0; mŒ1; mŒ2; : : : each of which can store an arbitrary integer, together with a program. A program consists of a finite sequence of instructions I0 ; I1 ; : : :, where each instruction is of one of the following types: arithmetic This type of instruction is of the form
˛ ? ˇ, where ? represents
3.2 Machine models and complexity theory
53
one of the operations addition, subtraction, multiplication, or integer division (i.e., b=c). The values ˛ and ˇ are of the form c, mŒa, or mŒmŒa, and is of the form mŒa or mŒmŒa, where c is an integer constant and a is a non-negative integer constant. Execution of this type of instruction causes the value ˛ ? ˇ to be evaluated and then stored in . branching This type of instruction is of the form IF ˛ Þ ˇ GOTO i , where i is the index of an instruction, and where Þ is one of the comparison operations D; ¤; <; >; ; , and ˛ and ˇ are as above. Execution of this type of instruction causes the “flow of control” to pass conditionally to instruction Ii . halt The HALT instruction halts the execution of the program. A RAM executes by executing instruction I0 , and continues to execute instructions, following branching instructions as appropriate, until a HALT instruction is executed. We do not specify input or output instructions, and instead assume that the input and output are to be found in memory cells at some prescribed locations, in some standardized format. To determine the running time of a program on a given input, we charge 1 unit of time to each instruction executed. This model of computation closely resembles a typical modern-day computer, except that we have abstracted away many annoying details. However, there are two details of real machines that cannot be ignored; namely, any real machine has a finite number of memory cells, and each cell can store numbers only in some fixed range. The first limitation must be dealt with by either purchasing sufficient memory or designing more space-efficient algorithms. The second limitation is especially annoying, as we will want to perform computations with quite large integers — much larger than will fit into any single memory cell of an actual machine. To deal with this limitation, we shall represent such large integers as vectors of digits to some fixed base, so that each digit is bounded so as to fit into a memory cell. This is discussed in more detail in the next section. The only other numbers we actually need to store in memory cells are “small” numbers representing array indices, counters, and the like, which hopefully will fit into the memory cells of actual machines. Below, we shall make a more precise, formal restriction on the magnitude of numbers that may be stored in memory cells. Even with these caveats and restrictions, the running time as we have defined it for a RAM is still only a rough predictor of performance on an actual machine. On a real machine, different instructions may take significantly different amounts
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Computing with large integers
of time to execute; for example, a division instruction may take much longer than an addition instruction. Also, on a real machine, the behavior of the cache may significantly affect the time it takes to load or store the operands of an instruction. Finally, the precise running time of an algorithm given by a high-level description will depend on the quality of the translation of this algorithm into “machine code.” However, despite all of these problems, it still turns out that measuring the running time on a RAM as we propose here is a good “first order” predictor of performance on real machines in many cases. Also, we shall only state the running time of an algorithm using a big-O estimate, so that implementation-specific constant factors are anyway “swept under the rug.” If we have an algorithm for solving a certain problem, we expect that “larger” instances of the problem will require more time to solve than “smaller” instances, and a general goal in the analysis of any algorithm is to estimate the rate of growth of the running time of the algorithm as a function of the size of its input. For this purpose, we shall simply measure the size of an input as the number of memory cells used to represent it. Theoretical computer scientists sometimes equate the notion of “efficient” with “polynomial time” (although not everyone takes theoretical computer scientists very seriously, especially on this point): a polynomial-time algorithm is one whose running time on inputs of size n is at most anb C c, for some constants a, b, and c (a “real” theoretical computer scientist will write this as nO.1/ ). Furthermore, we also require that for a polynomial-time algorithm, all 0 numbers stored in memory are at most a0 nb C c 0 in absolute value, for some constants a0 , b 0 , and c 0 . Even for algorithms that are not polynomial time, we shall insist that after executing t instructions, all numbers stored in memory are at most 0 a0 .n C t/b C c 0 in absolute value, for some constants a0 , b 0 , and c 0 . Note that in defining the notion of polynomial time on a RAM, it is essential that we restrict the magnitude of numbers that may be stored in the machine’s memory cells, as we have done above. Without this restriction, a program could perform arithmetic on huge numbers, being charged just one unit of time for each arithmetic operation— not only is this intuitively “wrong,” it is possible to come up with programs that solve some problems using a polynomial number of arithmetic operations on huge numbers, and these problems cannot otherwise be solved in polynomial time (see §3.6). 3.3 Basic integer arithmetic We will need algorithms to manipulate very large integers. Since such integers will exceed the word-size of actual machines, and to satisfy the formal requirements of our random access model of computation, we shall represent large integers as
3.3 Basic integer arithmetic
55
vectors of digits to some base B, along with a bit indicating the sign. That is, for a 2 Z, if we write aD˙
k X1
ai B i D ˙.ak
1 a1 a0 /B ;
i D0
where 0 ai < B for i D 0; : : : ; k 1, then a will be represented in memory as a data structure consisting of the vector of base-B digits a0 ; : : : ; ak 1 , along with a “sign bit” to indicate the sign of a. To ensure a unique representation, if a is non-zero, then the high-order digit ak 1 in this representation should be non-zero. For our purposes, we shall consider B to be a constant, and moreover, a power of 2. The choice of B as a power of 2 is convenient for a number of technical reasons. A note to the reader: If you are not interested in the low-level details of algorithms for integer arithmetic, or are willing to take them on faith, you may safely skip ahead to §3.3.5, where the results of this section are summarized. We now discuss in detail basic arithmetic algorithms for unsigned (i.e., nonnegative) integers — these algorithms work with vectors of base-B digits, and except where explicitly noted, we do not assume the high-order digits of the input vectors are non-zero, nor do these algorithms ensure that the high-order digit of the output vector is non-zero. These algorithms can be very easily adapted to deal with arbitrary signed integers, and to take proper care that the high-order digit of the vector representing a non-zero number is non-zero (the reader is asked to fill in these details in some of the exercises below). All of these algorithms can be implemented directly in a programming language that provides a “built-in” signed integer type that can represent all integers of absolute value less than B 2 , and that provides the basic arithmetic operations (addition, subtraction, multiplication, integer division). So, for example, using the C or Java programming language’s int type on a typical 32-bit computer, we could take B D 215 . The resulting software would be reasonably efficient, but certainly not the best possible. Suppose we have the base-B representations of two unsigned integers a and b. We present algorithms to compute the base-B representation of a C b, a b, a b, ba=bc, and a mod b. To simplify the presentation, for integers x; y with y ¤ 0, we write QuoRem.x; y/ to denote the quotient/remainder pair .bx=yc; x mod y/. 3.3.1 Addition Let a D .ak 1 a0 /B and b D .b` 1 b0 /B be unsigned integers. Assume that k ` 1 (if k < `, then we can just swap a and b). The sum c WD a C b is of the form c D .ck ck 1 c0 /B . Using the standard “paper-and-pencil”
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Computing with large integers
method (adapted from base-10 to base-B, of course), we can compute the base-B representation of a C b in time O.k/, as follows: 0 0 to ` tmp ai for i ` to k tmp ai ck carry carry for i
1 do C bi C carry, .carry; ci / QuoRem.tmp; B/ 1 do C carry, .carry; ci / QuoRem.tmp; B/
Note that in every loop iteration, the value of carry is 0 or 1, and the value tmp lies between 0 and 2B 1. 3.3.2 Subtraction Let a D .ak 1 a0 /B and b D .b` 1 b0 /B be unsigned integers. Assume that k ` 1. To compute the difference c WD a b, we may use the same algorithm as above, but with the expression “ai C bi ” replaced by “ai bi .” In every loop iteration, the value of carry is 0 or 1, and the value of tmp lies between B and B 1. If a b, then ck D 0 (i.e., there is no carry out of the last loop iteration); otherwise, ck D 1 (and b a D B k .ck 1 c0 /B , which can be computed with another execution of the subtraction routine). 3.3.3 Multiplication Let a D .ak 1 a0 /B and b D .b` 1 b0 /B be unsigned integers, with k 1 and ` 1. The product c WD a b is of the form .ckC` 1 c0 /B , and may be computed in time O.k`/ as follows: for i for i
0 to k C ` 1 do ci 0 0 to k 1 do carry 0 for j 0 to ` 1 do tmp ai bj C ci Cj C carry .carry; ci Cj / QuoRem.tmp; B/ ci C` carry
Note that at every step in the above algorithm, the value of carry lies between 0 and B 1, and the value of tmp lies between 0 and B 2 1.
3.3 Basic integer arithmetic
57
3.3.4 Division with remainder Let a D .ak 1 a0 /B and b D .b` 1 b0 /B be unsigned integers, with k 1, ` 1, and b` 1 ¤ 0. We want to compute q and r such that a D bq C r and 0 r < b. Assume that k `; otherwise, a < b, and we can just set q 0 and r a. The quotient q will have at most m WD k ` C 1 base-B digits. Write q D .qm 1 q0 /B . At a high level, the strategy we shall use to compute q and r is the following: r a for i m 1 down to 0 do qi br =B i bc r r B i qi b One easily verifies by induction that at the beginning of each loop iteration, we have 0 r < B i C1 b, and hence each qi will be between 0 and B 1, as required. Turning the above strategy into a detailed algorithm takes a bit of work. In particular, we want an easy way to compute br =B i bc. Now, we could in theory just try all possible choices for qi — this would take time O.B`/, and viewing B as a constant, this is O.`/. However, this is not really very desirable from either a practical or theoretical point of view, and we can do much better with just a little effort. We shall first consider a special case; namely, the case where ` D 1. In this case, the computation of the quotient br =B i bc is facilitated by the following, which essentially tells us that this quotient is determined by the two high-order digits of r: Theorem 3.1. Let x and y be integers such that 0 x D x 0 2n C s and 0 < y D y 0 2n for some integers n; s; x 0 ; y 0 , with n 0 and 0 s < 2n . Then bx=yc D bx 0 =y 0 c. Proof. We have x x0 s x0 D 0 C 0 n 0: y y y2 y It follows immediately that bx=yc bx 0 =y 0 c. We also have 0 x x0 s x0 1 x y0 1 1 C C 0: D 0C 0 n < 0C 0 0 0 y y y2 y y y y y Thus, we have x=y < bx 0 =y 0 c C 1; and hence, bx=yc bx 0 =y 0 c.
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Computing with large integers
From this theorem, one sees that the following algorithm correctly computes the quotient and remainder in time O.k/ (in the case ` D 1): r 0 for i k 1 down to 0 do tmp r B C ai .qi ; r/ QuoRem.tmp; b0 / output the quotient q D .qk 1 q0 /B and the remainder r Note that in every loop iteration, the value of r lies between 0 and b0 B and the value of tmp lies between 0 and B b0 C .B 1/ B 2 1.
1,
That takes care of the special case where ` D 1. Now we turn to the general case ` 1. In this case, we cannot so easily get the digits qi of the quotient, but we can still fairly easily estimate these digits, using the following: Theorem 3.2. Let x and y be integers such that 0 x D x 0 2n C s and 0 < y D y 0 2n C t for some integers n; s; t; x 0 ; y 0 with n 0, 0 s < 2n , and 0 t < 2n . Further, suppose that 2y 0 x=y. Then bx=yc bx 0 =y 0 c bx=yc C 2: Proof. We have x=y x =y 0 2n , and so bx=yc bx =y 0 2n c, and by the previous theorem, bx =y 0 2n c D bx 0 =y 0 c. That proves the first inequality. For the second inequality, first note that from the definitions, we have x=y 0 x =.y 0 C 1/, which implies x 0 y xy 0 x 0. Further, 2y 0 x=y implies 2yy 0 x 0. So we have 2yy 0 x 0 x 0 y xy 0 x, which implies x=y x 0 =y 0 2, and hence bx=yc bx 0 =y 0 c 2. Based on this theorem, we first present an algorithm for division with remainder that works assuming that b is appropriately “normalized,” meaning that b` 1 2w 1 , where B D 2w . This algorithm is shown in Fig. 3.1. Some remarks are in order: 1. In line 4, we compute qi , which by Theorem 3.2 is greater than or equal to the true quotient digit, but exceeds this value by at most 2. 2. In line 5, we reduce qi if it is obviously too big. 3. In lines 6–10, we compute .ri C` ri /B
.ri C` ri /B
qi b:
In each loop iteration, the value of tmp lies between .B 2 and the value carry lies between .B 1/ and 0.
B/ and B
1,
59
3.3 Basic integer arithmetic
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
for i 0 to k 1 do ri ai rk 0 for i k ` down to 0 do qi b.ri C` B C ri C` 1 /=b` 1 c if qi B then qi B 1 carry 0 for j 0 to ` 1 do tmp ri Cj qi bj C carry .carry; ri Cj / QuoRem.tmp; B/ ri C` ri C` C carry while ri C` < 0 do carry 0 for j 0 to ` 1 do tmp ri Cj C bi C carry .carry; ri Cj / QuoRem.tmp; B/ ri C` ri C` C carry qi qi 1 output the quotient q D .qk ` q0 /B and the remainder r D .r` 1 r0 /B
Fig. 3.1. Division with Remainder Algorithm 4. If the estimate qi is too large, this is manifested by a negative value of ri C` at line 10. Lines 11–17 detect and correct this condition: the loop body here executes at most twice; in lines 12–16, we compute .ri C` ri /B
.ri C` ri /B C .b`
1 b0 /B :
Just as in the algorithm in §3.3.1, in every iteration of the loop in lines 13–15, the value of carry is 0 or 1, and the value tmp lies between 0 and 2B 1. It is quite easy to see that the running time of the above algorithm is O.` .k ` C 1//. Finally, consider the general case, where b may not be normalized. We multiply 0 both a and b by an appropriate value 2w , with 0 w 0 < w, obtaining a0 WD 0 0 a2w and b 0 WD 2w , where b 0 is normalized; alternatively, we can use a more efficient, special-purpose “left shift” algorithm to achieve the same effect. We then compute q and r 0 such that a0 D b 0 q C r 0 , using the above division algorithm
60
Computing with large integers 0
for the normalized case. Observe that q D ba0 =b 0 c D ba=bc, and r 0 D r2w , 0 where r D a mod b. To recover r, we simply divide r 0 by 2w , which we can do either using the above “single precision” division algorithm, or by using a specialpurpose “right shift” algorithm. All of this normalizing and denormalizing takes time O.k C `/. Thus, the total running time for division with remainder is still O.` .k ` C 1//. E XERCISE 3.17. Work out the details of algorithms for arithmetic on signed integers, using the above algorithms for unsigned integers as subroutines. You should give algorithms for addition, subtraction, multiplication, and division with remainder of arbitrary signed integers (for division with remainder, your algorithm should compute ba=bc and a mod b). Make sure your algorithm correctly computes the sign bit of the result, and also strips leading zero digits from the result. E XERCISE 3.18. Work out the details of an algorithm that compares two signed integers a and b, determining which of a < b, a D b, or a > b holds. E XERCISE 3.19. Suppose that we run the division with remainder algorithm in Fig. 3.1 for ` > 1 without normalizing b, but instead, we compute the value qi in line 4 as follows: qi
b.ri C` B 2 C ri C`
1B
C riC`
2 /=.b` 1 B
C b`
2 /c:
Show that qi is either equal to the correct quotient digit, or the correct quotient digit plus 1. Note that a limitation of this approach is that the numbers involved in the computation are larger than B 2 . E XERCISE 3.20. Work out the details for an algorithm that shifts a given unsigned integer a to the left by a specified number of bits s (i.e., computes b WD a 2s ). The running time of your algorithm should be linear in the number of digits of the output. E XERCISE 3.21. Work out the details for an algorithm that shifts a given unsigned integer a to the right by a specified number of bits s (i.e., computes b WD ba=2s c). The running time of your algorithm should be linear in the number of digits of the output. Now modify your algorithm so that it correctly computes ba=2s c for signed integers a. E XERCISE 3.22. This exercise is for C/Java programmers. Evaluate the C/Java expressions (-17) % 4;
(-17) & 3;
and compare these values with . 17/ mod 4. Also evaluate the C/Java expressions
3.3 Basic integer arithmetic
(-17) / 4;
61
(-17) >> 2;
and compare with b 17=4c. Explain your findings. E XERCISE 3.23. This exercise is also for C/Java programmers. Suppose that values of type int are stored using a 32-bit 2’s complement representation, and that all basic arithmetic operations are computed correctly modulo 232 , even if an “overflow” happens to occur. Also assume that double precision floating point has 53 bits of precision, and that all basic arithmetic operations give a result with a relative error of at most 2 53 . Also assume that conversion from type int to double is exact, and that conversion from double to int truncates the fractional part. Now, suppose we are given int variables a, b, and n, such that 1 < n < 230 , 0 a < n, and 0 b < n. Show that after the following code sequence is executed, the value of r is equal to .a b/ mod n: int q; q = (int) ((((double) a) * ((double) b)) / ((double) n)); r = a*b - q*n; if (r >= n) r = r - n; else if (r < 0) r = r + n;
3.3.5 Summary We now summarize the results of this section. For an integer a, we define its bit length, or simply, its length, which we denote by len.a/, to be the number of bits in the binary representation of jaj; more precisely, blog2 jajc C 1 if a ¤ 0, len.a/ WD 1 if a D 0. If len.a/ D `, we say that a is an `-bit integer. Notice that if a is a positive, `-bit integer, then log2 a < ` log2 a C 1, or equivalently, 2` 1 a < 2` . Assuming that arbitrarily large integers are represented as described at the beginning of this section, with a sign bit and a vector of base-B digits, where B is a constant power of 2, we may state the following theorem. Theorem 3.3. Let a and b be arbitrary integers. (i) We can compute a ˙ b in time O.len.a/ C len.b//. (ii) We can compute a b in time O.len.a/ len.b//. (iii) If b ¤ 0, we can compute the quotient q WD ba=bc and the remainder r WD a mod b in time O.len.b/ len.q//.
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Computing with large integers
Note the bound O.len.b/ len.q// in part (iii) of this theorem, which may be significantly less than the bound O.len.a/ len.b//. A good way to remember this bound is as follows: the time to compute the quotient and remainder is roughly the same as the time to compute the product bq appearing in the equality a D bq C r. This theorem does not explicitly refer to the base B in the underlying implementation. The choice of B affects the values of the implied big-O constants; while in theory, this is of no significance, it does have a significant impact in practice. From now on, we shall (for the most part) not worry about the implementation details of long-integer arithmetic, and will just refer directly this theorem. However, we will occasionally exploit some trivial aspects of our data structure for representing large integers. For example, it is clear that in constant time, we can determine the sign of a given integer a, the bit length of a, and any particular bit of the binary representation of a; moreover, as discussed in Exercises 3.20 and 3.21, multiplications and divisions by powers of 2 can be computed in linear time via “left shifts” and “right shifts.” It is also clear that we can convert between the base2 representation of a given integer and our implementation’s internal representation in linear time (other conversions may take longer — see Exercise 3.32). We wish to stress the point that efficient algorithms on large integers should run in time bounded by a polynomial in the bit lengths of the inputs, rather than their magnitudes. For example, if the input to an algorithm is an `-bit integer n, and if the algorithm runs in time O.`2 /, it will easily be able to process 1000-bit inputs in a reasonable amount of time (a fraction of a second) on typical, modern computer. However, if the algorithm runs in time, say, O.n1=2 /, this means that on 1000-bit inputs, it will take roughly 2500 computing steps, which even on the fastest computer available today or in the foreseeable future, will still be running long after our solar system no longer exists. A note on notation: “len” and “log.” In expressing the running times of algorithms in terms of an input a, we generally prefer to write len.a/ rather than log a. One reason is esthetic: writing len.a/ stresses the fact that the running time is a function of the bit length of a. Another reason is technical: for big-O estimates involving functions on an arbitrary domain, the appropriate inequalities should hold throughout the domain, and for this reason, it is very inconvenient to use functions, like log, which vanish or are undefined on some inputs.
E XERCISE 3.24. Let a; b 2 Z with a b > 0, and let q WD ba=bc. Show that len.a/ len.b/ 1 len.q/ len.a/ len.b/ C 1.
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3.3 Basic integer arithmetic
E XERCISE 3.25. Let n1 ; : : : ; nk be positive integers. Show that k X i D1
len.ni /
k len
Y k
ni
i D1
k X
len.ni /:
i D1
E XERCISE 3.26. Show that the product n of integers n1 ; : : : ; nk , with each ni > 1, can be computed in time O.len.n/2 /. E XERCISE 3.27. Show that given integers n1 ; : : : ; nk , with each ni > 1, and an Q integer a, where 0 a < n and n WD i ni , we can compute the k integers a mod ni , for i D 1; : : : ; k, in time O.len.n/2 /. E XERCISE 3.28. Show that given integers n1 ; : : : ; nk , with each ni > 1, we Q can compute the k integers n=ni , for i D 1; : : : ; k, where n WD i ni , in time O.len.n/2 /. p E XERCISE 3.29. This exercise develops an algorithm to compute b nc for a given positive integer n. Consider the following algorithm: k b.len.n/ 1/=2c, m 2k for i k 1 down to 0 do if .m C 2i /2 n then m m C 2i output m p (a) Show that this algorithm correctly computes b nc. (b) In a straightforward implementation of this algorithm, each loop iteration takes time O.len.n/2 /, yielding a total running time of O.len.n/3 /. Give a more careful implementation, so that each loop iteration takes time O.len.n//, yielding a total running time is O.len.n/2 /. E XERCISE 3.30. Modify the algorithm given on the previous exercise so that that given positive integers n and e, with n 2e , it computes bn1=e c in time O.len.n/3 =e/. E XERCISE 3.31. An integer n > 1 is called a perfect power if n D ab for integers a > 1 and b > 1. Using the algorithm from the previous exercise, design an efficient algorithm that determines if a given n is a perfect power, and if so, also computes a and b such that n D ab , where a > 1, b > 1, and a is as small as possible. Your algorithm should run in time O.`3 len.`//, where ` WD len.n/. E XERCISE 3.32. Show how to convert (in both directions) between the base-10 representation and our implementation’s internal representation of an integer n in time O.len.n/2 /.
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Computing with large integers
3.4 Computing in Zn Let n > 1. For every ˛ 2 Zn , there exists a unique integer a 2 f0; : : : ; n 1g such that ˛ D Œan ; we call this integer a the canonical representative of ˛, and denote it by rep.˛/. For computational purposes, we represent elements of Zn by their canonical representatives. Addition and subtraction in Zn can be performed in time O.len.n//: given ˛; ˇ 2 Zn , to compute rep.˛ C ˇ/, we simply compute the integer sum rep.˛/ C rep.ˇ/, subtracting n if the result is greater than or equal to n; similarly, to compute rep.˛ ˇ/, we compute the integer difference rep.˛/ rep.ˇ/, adding n if the result is negative. Multiplication in Zn can be performed in time O.len.n/2 /: given ˛; ˇ 2 Zn , we compute rep.˛ ˇ/ as rep.˛/ rep.ˇ/ mod n, using one integer multiplication and one division with remainder. A note on notation: “rep,” “mod,” and “Œn .” In describing algorithms, as well as in other contexts, if ˛; ˇ are elements of Zn , we may write, for example, ˛ C ˇ or ˛ˇ, and it is understood that elements of Zn are represented by their canonical representatives as discussed above, and arithmetic on canonical representatives is done modulo n. Thus, we have in mind a “strongly typed” language for our pseudo-code that makes a clear distinction between integers in the set f0; : : : ; n 1g and elements of Zn . If a 2 Z, we can convert a to an object ˛ 2 Zn by writing ˛ Œan , and if a 2 f0; : : : ; n 1g, this type conversion is purely conceptual, involving no actual computation. Conversely, if ˛ 2 Zn , we can convert ˛ to an object a 2 f0; : : : ; n 1g, by writing a rep.˛/; again, this type conversion is purely conceptual, and involves no actual computation. It is perhaps also worthwhile to stress the distinction between a mod n and Œan — the former denotes an element of the set f0; : : : ; n 1g, while the latter denotes an element of Zn .
Another interesting problem is exponentiation in Zn : given ˛ 2 Zn and a nonnegative integer e, compute ˛ e 2 Zn . Perhaps the most obvious way to do this is to iteratively multiply by ˛ a total of e times, requiring time O.e len.n/2 /. For small values of e, this is fine; however, a much faster algorithm, the repeated-squaring algorithm, computes ˛ e using just O.len.e// multiplications in Zn , thus taking time O.len.e/ len.n/2 /. This method is based on the following observation. Let e D .b` 1 b0 /2 be the binary expansion of e (where b0 is the low-order bit). For i D 0; : : : ; `, define ei WD be=2i c; the binary expansion of ei is ei D .b` 1 bi /2 . Also define ˇi WD ˛ ei for i D 0; : : : ; `, so ˇ` D 1 and ˇ0 D ˛ e . Then we have ei D 2ei C1 C bi and ˇi D ˇi2C1 ˛ bi for i D 0; : : : ; `
1:
This observation yields the following algorithm for computing ˛ e : The repeated-squaring algorithm. On input ˛; e, where ˛ 2 Zn and e is a non-
3.4 Computing in Zn
negative integer, do the following, where e D .b` of e:
1 b0 /2
65
is the binary expansion
ˇ Œ1n for i ` 1 down to 0 do ˇ ˇ2 if bi D 1 then ˇ ˇ˛ output ˇ It is clear that when this algorithm terminates, we have ˇ D ˛ e , and that the running-time estimate is as claimed above. Indeed, the algorithm uses ` squarings in Zn , and at most ` additional multiplications in Zn . Example 3.5. Suppose e D 37 D .100101/2 . The above algorithm performs the following operations in this case: ˇ ˇ ˇ ˇ ˇ ˇ ˇ
Œ1 ˇ2, ˇ ˇ2 ˇ2 ˇ2, ˇ ˇ2 ˇ2, ˇ
ˇ˛
ˇ˛ ˇ˛
// computed exponent (in binary) // 0 // 1 // 10 // 100 // 1001 // 10010 // 100101
The repeated-squaring algorithm has numerous applications. We mention a few here, but we will see many more later on. Computing multiplicative inverses in Zp . Suppose we are given a prime p and an element ˛ 2 Zp , and we want to compute ˛ 1 . By Euler’s theorem (Theorem 2.13), we have ˛ p 1 D 1, and multiplying this equation by ˛ 1 , we obtain ˛ p 2 D ˛ 1 . Thus, we can use the repeated-squaring algorithm to compute ˛ 1 by raising ˛ to the power p 2. This algorithm runs in time O.len.p/3 /. While this is a reasonably efficient algorithm, we will develop an even more efficient method in the next chapter, using Euclid’s algorithm (which also works with any modulus, not just a prime modulus). Testing quadratic residuosity. Suppose we are given an odd prime p and an element ˛ 2 Zp , and we want to test whether ˛ 2 .Zp /2 . By Euler’s criterion (Theorem 2.21), we have ˛ 2 .Zp /2 if and only if ˛ .p 1/=2 D 1. Thus, we can use the repeated-squaring algorithm to test if ˛ 2 .Zp /2 by raising ˛ to the power .p 1/=2. This algorithm runs in time O.len.p/3 /. While this is a reasonably
66
Computing with large integers
efficient algorithm, we will develop an even more efficient method later in the text (in Chapter 12). Testing for primality. Suppose we are given an integer n > 1, and we want to determine whether n is prime or composite. For large n, searching for prime factors of n is hopelessly impractical. A better idea is to use Euler’s theorem, combined with the repeated-squaring algorithm: we know that if n is prime, then every non-zero ˛ 2 Zn satisfies ˛ n 1 D 1. Conversely, if n is composite, there exists a non-zero ˛ 2 Zn such that ˛ n 1 ¤ 1 (see Exercise 2.27). This suggests the following “trial and error” strategy for testing if n is prime: repeat k times choose ˛ 2 Zn n fŒ0g compute ˇ ˛n 1 if ˇ ¤ 1 output “composite” and halt output “maybe prime” As stated, this is not a fully specified algorithm: we have to specify the loopiteration parameter k, and more importantly, we have to specify a procedure for choosing ˛ in each loop iteration. One approach might be to just try ˛ D Œ1; Œ2; Œ3; : : : : Another might to be to choose ˛ at random in each loop iteration: this would be an example of a probabilistic algorithm (a notion we shall discuss in detail in Chapter 9). In any case, if the algorithm outputs “composite,” we may conclude that n is composite (even though the algorithm does not find a non-trivial factor of n). However, if the algorithm completes all k loop iterations and outputs “maybe prime,” it is not clear what we should conclude: certainly, we have some reason to suspect that n is prime, but not really a proof; indeed, it may be the case that n is composite, but we were just unlucky in all of our choices for ˛. Thus, while this rough idea does not quite give us an effective primality test, it is not a bad start, and is the basis for several effective primality tests (a couple of which we shall discuss in detail in Chapters 10 and 21). E XERCISE 3.33. The repeated-squaring algorithm we have presented here processes the bits of the exponent from left to right (i.e., from high order to low order). Develop an algorithm for exponentiation in Zn with similar complexity that processes the bits of the exponent from right to left. E XERCISE 3.34. Show that given a prime p, ˛ 2 Zp , and an integer e p, we can compute ˛ e in time O.len.e/ len.p/ C len.p/3 /. The following exercises develop some important efficiency improvements to the basic repeated-squaring algorithm.
3.4 Computing in Zn
67
E XERCISE 3.35. The goal of this exercise is to develop a “2t -ary” variant of the above repeated-squaring algorithm, in which the exponent is effectively treated as a number in base 2t , for some parameter t , rather than in base 2. Let ˛ 2 Zn and let e be a positive integer of length `. Let us write e in base 2t as e D .ek e0 /2t , where ek ¤ 0. Consider the following algorithm: compute a table of values T Œ0 : : : 2t 1, where T Œj WD ˛ j for j D 0; : : : ; 2t ˇ T Œek for i k 1 down to 0 do t ˇ ˇ 2 T Œei
1
(a) Show that this algorithm correctly computes ˛ e , and work out the implementation details, showing that it may be implemented so as to use at most ` squarings and 2t C `=t C O.1/ additional multiplications in Zn . (b) Show that by appropriately choosing the parameter t, we can bound the number of multiplications in Zn (besides the squarings) by O.`= len.`//. Thus, from an asymptotic point of view, the cost of exponentiation is essentially the cost of about ` squarings in Zn . (c) Improve the algorithm so that it only uses at most ` squarings and 2t 1 C `=t C O.1/ additional multiplications in Zn . Hint: build a table that cont tains only the odd powers of ˛ among ˛ 0 ; ˛ 1 ; : : : ; ˛ 2 1 . E XERCISE 3.36. Suppose we are given ˛1 ; : : : ; ˛k 2 Zn , along with non-negative integers e1 ; : : : ; ek , where len.ei / ` for i D 1; : : : ; k. Show how to compute ˇ WD ˛1e1 ˛kek , using at most ` squarings and ` C 2k additional multiplications in Zn . Your algorithm should work in two phases: the first phase uses only the values ˛1 ; : : : ; ˛k , and performs at most 2k multiplications in Zn ; in the second phase, the algorithm computes ˇ, using the exponents e1 ; : : : ; ek , along with the data computed in the first phase, and performs at most ` squarings and ` additional multiplications in Zn . E XERCISE 3.37. Suppose that we are to compute ˛ e , where ˛ 2 Zn , for many exponents e of length at most `, but with ˛ fixed. Show that for every positive integer parameter k, we can make a pre-computation (depending on ˛, `, and k) that uses at most ` squarings and 2k additional multiplications in Zn , so that after the pre-computation, we can compute ˛ e for every exponent e of length at most ` using at most `=k C O.1/ squarings and `=k C O.1/ additional multiplications in Zn . Hint: use the algorithm in the previous exercise. E XERCISE 3.38. Suppose we are given ˛ 2 Zn , along with non-negative integers
68
Computing with large integers
e1 ; : : : ; er , where len.ei / ` for i D 1; : : : ; r, and r D O.len.`//. Using the previous exercise, show how to compute the r values ˛ e1 ; : : : ; ˛ er , using O.`/ multiplications in Zn . E XERCISE 3.39. Suppose we are given ˛ 2 Zn , along with integers m1 ; : : : ; mr , each greater than 1, whose product is m. Also, for i D 1; : : : ; r, define mi WD m=mi . Show how to compute the r values ˛ m1 ; : : : ; ˛ mr , using a total of O.len.r/`/ multiplications in Zn , where ` WD len.m/. Hint: divide and conquer. Note that if r D O.len.`//, then using the previous exercise, we can do this using just O.`/ multiplications. E XERCISE 3.40. Let k be a constant, positive integer. Suppose we are given ˛1 ; : : : ; ˛k 2 Zn , along with non-negative integers e1 ; : : : ; ek , where len.ei / ` for i D 1; : : : ; k. Show how to compute the value ˛1e1 ˛kek , using at most ` squarings and O.`= len.`// additional multiplications in Zn . Hint: develop a 2t ary version of the algorithm in Exercise 3.36. 3.5 Faster integer arithmetic ./ The quadratic-time algorithms presented in §3.3 for integer multiplication and division are by no means the fastest possible. The next exercise develops a faster multiplication algorithm. E XERCISE 3.41. Suppose we have two positive integers a and b, each of length at most `, such that a D a1 2k C a0 and b D b1 2k C b0 , where 0 a0 < 2k and 0 b0 < 2k . Then ab D a1 b1 22k C .a0 b1 C a1 b0 /2k C a0 b0 : Show how to compute the product ab in time O.`/, given the products a0 b0 , a1 b1 , and .a0 a1 /.b0 b1 /. From this, design a recursive algorithm that computes ab in time O.`log2 3 /. (Note that log2 3 1:58:) The algorithm in the previous is also not the best possible. In fact, it is possible to multiply two integers of length at most ` on a RAM in time O.`/, but we do not explore this any further here (see §3.6). The following exercises explore the relationship between integer multiplication and related problems. We assume that we have an algorithm that multiplies two integers of length at most ` in time at most M.`/. It is convenient (and reasonable) to assume that M is a well-behaved complexity function. By this, we mean that M maps positive integers to positive real numbers, such that for some constant
3.5 Faster integer arithmetic ./
69
1, and all positive integers a and b, we have 1
M.a C b/ : M.a/ C M.b/
E XERCISE 3.42. Show that if M is a well-behaved complexity function, then it is strictly increasing. E XERCISE 3.43. Show that if N.`/ WD M.`/=` is a non-decreasing function, and M.2`/=M.`/ D O.1/, then M is a well-behaved complexity function. E XERCISE 3.44. Let ˛ > 0, ˇ 1, 0, ı 0 be real constants. Show that M.`/ WD ˛`ˇ len.`/ len.len.`//ı is a well-behaved complexity function. E XERCISE 3.45. Show that given integers n > 1 and e > 1, we can compute ne in time O.M.len.ne ///. E XERCISE 3.46. Give an algorithm for Exercise 3.26 that runs in time O.M.len.n// len.k//: Hint: divide and conquer. E XERCISE 3.47. In the previous exercise, suppose all the inputs ni have the same length, and that M.`/ D ˛`ˇ , where ˛ and ˇ are constants with ˛ > 0 and ˇ > 1. Show that your algorithm runs in time O.M.len.n///. E XERCISE 3.48. We can represent a “floating point” number zO as a pair .a; e/, where a and e are integers — the value of zO is the rational number a2e , and we call len.a/ the precision of z. O We say that zO is a k-bit approximation of a real number z if zO has precision k and zO D .1 C /z for some jj 2 kC1 . Show that given positive integers b and k, we can compute a k-bit approximation of 1=b in time O.M.k//. Hint: using Newton iteration, show how to go from a t -bit approximation of 1=b to a .2t 2/-bit approximation of 1=b, making use of just the high-order O.t/ bits of b, in time O.M.t //. Newton iteration is a general method of iteratively approximating a root of an equation f .x/ D 0 by starting with an initial approximation x0 , and computing subsequent approximations by the formula xi C1 D xi f .xi /=f 0 .xi /, where f 0 .x/ is the derivative of f .x/. For this exercise, apply Newton iteration to the function f .x/ D x 1 b. E XERCISE 3.49. Using the result of the previous exercise, show that given positive integers a and b of bit length at most `, we can compute ba=bc and a mod b in time O.M.`//. From this, we see that up to a constant factor, division with remainder is no harder than multiplication.
70
Computing with large integers
E XERCISE 3.50. Using the result of the previous exercise, give an algorithm for Exercise 3.27 that runs in time O.M.len.n// len.k//. Hint: divide and conquer. E XERCISE 3.51. Give an algorithm for Exercise 3.29 that runs in time O.M.len.n///. Hint: Newton iteration. E XERCISE 3.52. Suppose we have an algorithm that computes the square of an `-bit integer in time S.`/, where S is a well-behaved complexity function. Show how to use this algorithm to compute the product of two arbitrary integers of length at most ` in time O.S.`//. E XERCISE 3.53. Give algorithms for Exercise 3.32 that run in time O.M.`/ len.`//, where ` WD len.n/. Hint: divide and conquer. 3.6 Notes Shamir [87] shows how to factor an integer in polynomial time on a RAM, but where the numbers stored in the memory cells may have exponentially many bits. As there is no known polynomial-time factoring algorithm on any realistic machine, Shamir’s algorithm demonstrates the importance of restricting the sizes of numbers stored in the memory cells of our RAMs to keep our formal model realistic. The most practical implementations of algorithms for arithmetic on large integers are written in low-level “assembly language,” specific to a particular machine’s architecture (e.g., the GNU Multi-Precision library GMP, available at www.swox.com/gmp). Besides the general fact that such hand-crafted code is more efficient than that produced by a compiler, there is another, more important reason for using such code. A typical 32-bit machine often comes with instructions that allow one to compute the 64-bit product of two 32-bit integers, and similarly, instructions to divide a 64-bit integer by a 32-bit integer (obtaining both the quotient and remainder). However, high-level programming languages do not (as a rule) provide any access to these low-level instructions. Indeed, we suggested in §3.3 using a value for the base B of about half the word-size of the machine, so as to avoid overflow. However, if one codes in assembly language, one can take B to be much closer to, or even equal to, the word-size of the machine. Since our basic algorithms for multiplication and division run in time quadratic in the number of base-B digits, the effect of doubling the bit-length of B is to decrease the running time of these algorithms by a factor of four. This effect, combined with the improvements one might typically expect from using assembly-language code, can easily lead to a five- to ten-fold decrease in the running time, compared to an imple-
3.6 Notes
71
mentation in a high-level language. This is, of course, a significant improvement for those interested in serious “number crunching.” The “classical,” quadratic-time algorithms presented here for integer multiplication and division are by no means the best possible: there are algorithms that are asymptotically faster. We saw this in the algorithm in Exercise 3.41, which was originally invented by Karatsuba [53] (although Karatsuba is one of two authors on this paper, the paper gives exclusive credit for this particular result to Karatsuba). That algorithm allows us to multiply two integers of length at most ` in time O.`log2 3 /. The fastest known algorithm for multiplying such integers on a RAM runs in time O.`/. This algorithm is due to Schönhage, and actually works on a very restricted type of RAM called a “pointer machine” (see Problem 12, Section 4.3.3 of Knuth [55]). See Exercise 17.26 later in this text for a much simpler (but heuristic) O.`/ multiplication algorithm. Another model of computation is that of Boolean circuits. In this model of computation, one considers families of Boolean circuits (with, say, the usual “and,” “or,” and “not” gates) that compute a particular function — for every input length, there is a different circuit in the family that computes the function on inputs that are bit strings of that length. One natural notion of complexity for such circuit families is the size of the circuit (i.e., the number of gates and wires in the circuit), which is measured as a function of the input length. For many years, the smallest known Boolean circuit that multiplies two integers of length at most ` was of size O.` len.`/ len.len.`///. This result was due to Schönhage and Strassen [84]. More recently, Fürer showed how to reduce this to O.` len.`/2O.log `/ / [37]. Here, the value of log n is defined as the minimum number of applications of the function log2 to the number n to obtain a number that less than or equal to 1. The function log is an extremely slow growing function, and is a constant for all practical purposes. It is hard to say which model of computation, the RAM or circuits, is “better.” On the one hand, the RAM very naturally models computers as we know them today: one stores small numbers, like array indices, counters, and pointers, in individual words of the machine, and processing such a number typically takes a single “machine cycle.” On the other hand, the RAM model, as we formally defined it, invites a certain kind of “cheating,” as it allows one to stuff O.len.`//-bit integers into memory cells. For example, even with the simple, quadratic-time algorithms for integer arithmetic discussed in §3.3, we can choose the base B to have len.`/ bits, in which case these algorithms would run in time O..`= len.`//2 /. However, just to keep things simple, we have chosen to view B as a constant (from a formal, asymptotic point of view). In the remainder of this text, unless otherwise specified, we shall always use
72
Computing with large integers
the classical O.`2 / bounds for integer multiplication and division, which have the advantage of being both simple and reasonably reliable predictors of actual performance for small to moderately sized inputs. For relatively large numbers, experience shows that the classical algorithms are definitely not the best — Karatsuba’s multiplication algorithm, and related algorithms for division, start to perform significantly better than the classical algorithms on inputs of a thousand bits or so (the exact crossover depends on myriad implementation details). The even “faster” algorithms discussed above are typically not interesting unless the numbers involved are truly huge, of bit length around 105 –106 . Thus, the reader should bear in mind that for serious computations involving very large numbers, the faster algorithms are very important, even though this text does not discuss them at great length. For a good survey of asymptotically fast algorithms for integer arithmetic, see Chapter 9 of Crandall and Pomerance [30], as well as Chapter 4 of Knuth [55].
4 Euclid’s algorithm
In this chapter, we discuss Euclid’s algorithm for computing greatest common divisors. It turns out that Euclid’s algorithm has a number of very nice properties, and has applications far beyond that of just computing greatest common divisors. 4.1 The basic Euclidean algorithm We consider the following problem: given two non-negative integers a and b, compute their greatest common divisor, gcd.a; b/. We can do this using the well-known Euclidean algorithm, also called Euclid’s algorithm. The basic idea of Euclid’s algorithm is the following. Without loss of generality, we may assume that a b 0. If b D 0, then there is nothing to do, since in this case, gcd.a; 0/ D a. Otherwise, if b > 0, we can compute the integer quotient q WD ba=bc and remainder r WD a mod b, where 0 r < b. From the equation a D bq C r; it is easy to see that if an integer d divides both b and r, then it also divides a; likewise, if an integer d divides a and b, then it also divides r. From this observation, it follows that gcd.a; b/ D gcd.b; r/, and so by performing a division, we reduce the problem of computing gcd.a; b/ to the “smaller” problem of computing gcd.b; r/. The following theorem develops this idea further: Theorem 4.1. Let a; b be integers, with a b 0. Using the division with remainder property, define the integers r0 ; r1 ; : : : ; r`C1 , and q1 ; : : : ; q` , where ` 0, as follows:
73
74
Euclid’s algorithm
a D r0 ; b D r1 ; r0 D r1 q1 C r2 :: :
.0 < r2 < r1 /;
D ri qi C ri C1 :: :
.0 < ri C1 < ri /;
2
D r`
.0 < r` < r`
1
D r` q`
ri
1
r` r`
1 q` 1
C r`
1 /;
.r`C1 D 0/:
Note that by definition, ` D 0 if b D 0, and ` > 0, otherwise. Then we have rp ` D gcd.a; b/. Moreover, if b > 0, then ` log b= log C 1, where WD .1 C 5/=2 1:62. Proof. For the first statement, one sees that for i D 1; : : : ; `, we have ri 1 D ri qi C ri C1 , from which it follows that the common divisors of ri 1 and ri are the same as the common divisors of ri and ri C1 , and hence gcd.ri 1 ; ri / D gcd.ri ; ri C1 /. From this, it follows that gcd.a; b/ D gcd.r0 ; r1 / D gcd.r` ; r`C1 / D gcd.r` ; 0/ D r` : To prove the second statement, assume that b > 0, and hence ` > 0. If ` D 1, the statement is obviously true, so assume ` > 1. We claim that for i D 0; : : : ; ` 1, we have r` i i . The statement will then follow by setting i D ` 1 and taking logarithms. We now prove the above claim. For i D 0 and i D 1, we have r` 1 D 0 and r` For i D 2; : : : ; ` have r`
i
r`
1
r` C 1 2 1 :
1, using induction and applying the fact that 2 D C 1, we .i 1/
C r`
.i 2/
i
1
C i
2
D i
2
.1 C / D i ;
which proves the claim. Example 4.1. Suppose a D 100 and b D 35. Then the numbers appearing in Theorem 4.1 are easily computed as follows: i ri qi
0 100
1 35 2
2 30 1
3 5 6
4 0
4.1 The basic Euclidean algorithm
75
So we have gcd.a; b/ D r3 D 5. We can easily turn the scheme described in Theorem 4.1 into a simple algorithm: Euclid’s algorithm. On input a; b, where a and b are integers such that a b 0, compute d D gcd.a; b/ as follows: r a; r 0 b while r 0 ¤ 0 do r 00 r mod r 0 .r; r 0 / .r 0 ; r 00 / d r output d We now consider the running time of Euclid’s algorithm. Naively, one could estimate this as follows. Suppose a and b are k-bit numbers. The number of divisions performed by the algorithm is the number ` in Theorem 4.1, which is O.k/. Moreover, each division involves numbers of k bits or fewer in length, and so takes time O.k 2 /. This leads to a bound on the running time of O.k 3 /. However, as the following theorem shows, this cubic running time bound is well off the mark. Intuitively, this is because the cost of performing a division depends on the length of the quotient: the larger the quotient, the more expensive the division, but also, the more progress the algorithm makes towards termination. Theorem 4.2. Euclid’s algorithm runs in time O.len.a/ len.b//. Proof. We may assume that b > 0. With notation as in Theorem 4.1, the running time is O.T /, where T D
` X
len.ri / len.qi / len.b/
i D1
` X
len.qi /
i D1
` X len.b/ .len.ri
1/
len.ri / C 1/ (see Exercise 3.24)
i D1
D len.b/.len.r0 /
len.r` / C `/ (telescoping the sum)
len.b/.len.a/ C log b= log C 1/ (by Theorem 4.1) D O.len.a/ len.b//: E XERCISE 4.1. With notation as in Theorem 4.1, show that for each i D 1; : : : ; `, we have ri C1 ri 1 =2. Thus, with every two division steps, the bit length of the remainder drops by at least 1. This leads an alternative way to bound the number of divisions by O.len.b//, but with a larger constant.
76
Euclid’s algorithm
E XERCISE 4.2. Show how to compute lcm.a; b/ in time O.len.a/ len.b//. E XERCISE 4.3. Let a; b 2 Z with a b > 0, and let d WD gcd.a; b/, a0 WD a=d , and b 0 WD b=d . Show that if we run Euclid’s algorithm on input a; b, then (a) it performs at most log b 0 = log C 1 divisions; (b) its running time is O.len.a0 / len.b//. E XERCISE 4.4. Let ` be a positive integer. Show that there exist integers a; b with a b 0 and ` log b= log , such that Euclid’s algorithm on input a; b performs exactly ` divisions. Thus, the bound in Theorem 4.1 on the number of divisions is essentially tight. E XERCISE 4.5. This exercise looks at an alternative algorithm for computing gcd.a; b/, called the binary gcd algorithm. This algorithm avoids complex operations, such as division and multiplication; instead, it relies only on subtraction, and division and multiplication by powers of 2, which assuming a binary representation of integers (as we are) can be very efficiently implemented using “right shift” and “left shift” operations. The algorithm takes positive integers a and b as input, and runs as follows: r a; r 0 b; e 0 while 2 j r and 2 j r 0 do r r=2; r 0 repeat while 2 j r do r r=2 0 0 while 2 j r do r r 0 =2 0 0 if r < r then .r; r / .r 0 ; r/ r0 r0 r 0 until r D 0 d 2e r output d
r 0 =2; e
eC1
Show that this algorithm correctly computes gcd.a; b/, and runs in time O.k 2 /, where k WD max.len.a/; len.b//. 4.2 The extended Euclidean algorithm Let a and b be integers, and let d WD gcd.a; b/. We know by Theorem 1.8 that there exist integers s and t such that as C bt D d . The extended Euclidean algorithm allows us to efficiently compute s and t. The following theorem defines the quantities computed by this algorithm, and states a number of important facts about them—these will play a crucial role, both in the analysis of the running time
77
4.2 The extended Euclidean algorithm
of the algorithm, as well as in applications of the algorithm that we will discuss later. Theorem 4.3. Let a, b, r0 ; : : : ; r`C1 and q1 ; : : : ; q` be as in Theorem 4.1. Define integers s0 ; : : : ; s`C1 and t0 ; : : : ; t`C1 as follows: s0 WD 1;
t0 WD 0;
s1 WD 0;
t1 WD 1;
si C1 WD si
ti C1 WD ti
si qi ;
1
1
.i D 1; : : : ; `/:
ti qi
Then (i) for i D 0; : : : ; ` C 1, we have asi C bti D ri ; in particular, as` C bt` D gcd.a; b/; ti si C1 D . 1/i ;
(ii) for i D 0; : : : ; `, we have si ti C1
(iii) for i D 0; : : : ; ` C 1, we have gcd.si ; ti / D 1; (iv) for i D 0; : : : ; `, we have ti ti C1 0 and jti j jti C1 j; for i D 1; : : : ; `, we have si si C1 0 and jsi j jsi C1 j; (v) for i D 1; : : : ; ` C 1, we have ri
1 jti j
a and ri
1 jsi j
b;
(vi) if a > 0, then for i D 1; : : : ; ` C 1, we have jti j a and jsi j b; if a > 1 and b > 0, then jt` j a=2 and js` j b=2. Proof. (i) is easily proved by induction on i . For i D 0; 1, the statement is clear. For i D 2; : : : ; ` C 1, we have asi C bti D a.si
2
si
D .asi
2
C bti
D ri
ri
2
1 qi 1 / 2/
1 qi 1
C b.ti
.asi
1
2
ti
C bti
1 qi 1 /
1 /qi 1
(by induction)
D ri : (ii) is also easily proved by induction on i . For i D 0, the statement is clear. For i D 1; : : : ; `, we have si ti C1
ti si C1 D si .ti
1
D
.si
D
. 1/i
ti qi /
1 ti
ti 1
ti .si 1 si /
1
si qi /
(after expanding and simplifying)
(by induction)
i
D . 1/ : (iii) follows directly from (ii). For (iv), one can easily prove both statements by induction on i . The statement involving the ti ’s is clearly true for i D 0; for i D 1; : : : ; `, we have ti C1 D
78
Euclid’s algorithm
ti 1 ti qi , and since by the induction hypothesis ti 1 and ti have opposite signs and jti j jti 1 j, it follows that jti C1 j D jti 1 j C jti jqi jti j, and that the sign of ti C1 is the opposite of that of ti . The proof of the statement involving the si ’s is the same, except that we start the induction at i D 1. For (v), one considers the two equations: asi
1
C bti
1
D ri
1;
asi C bti D ri : Subtracting ti 1 times the second equation from ti times the first, and applying (ii), we get ˙a D ti ri 1 ti 1 ri ; consequently, using the fact that ti and ti 1 have opposite sign, we obtain a D jti ri
ti
1
1 ri j
D jti jri
1
C jti
1 jri
jti jri
1:
The inequality involving si follows similarly, subtracting si 1 times the second equation from si times the first. (vi) follows from (v) and the following observations: if a > 0, then ri 1 > 0 for i D 1; : : : ; ` C 1; if a > 1 and b > 0, then ` > 0 and r` 1 2. Example 4.2. We continue with Example 4.1. The si ’s and ti ’s are easily computed from the qi ’s: i ri qi si ti So we have gcd.a; b/ D 5 D
0 100 1 0
1 35 2 0 1
2 30 1 1 -2
3 5 6 -1 3
4 0 7 -20
a C 3b.
We can easily turn the scheme described in Theorem 4.3 into a simple algorithm: The extended Euclidean algorithm. On input a; b, where a and b are integers such that a b 0, compute integers d , s, and t, such that d D gcd.a; b/ and as C bt D d , as follows: r a; r 0 b 0 s 1; s 0 t 0; t 0 1 while r 0 ¤ 0 do q br=r 0 c; r 00 .r; s; t; r 0 ; s 0 ; t 0 / d r output d; s; t
r mod r 0 .r 0 ; s 0 ; t 0 ; r 00 ; s
s 0 q; t
t 0 q/
79
4.2 The extended Euclidean algorithm
Theorem 4.4. The extended Euclidean algorithm runs in time O.len.a/ len.b//: Proof. We may assume that b > 0. It suffices to analyze the cost of computing the sequences fsi g and fti g. Consider first the cost of computing all of the ti ’s, P which is O.T /, where T D `iD1 len.ti / len.qi /: We have t1 D 1 and, by part (vi) of Theorem 4.3, we have jti j a for i D 2; : : : ; `. Arguing as in the proof of Theorem 4.2, we have T len.q1 / C len.a/
` X
len.qi /
i D2
len.a/ C len.a/.len.r1 /
len.r` / C `
1/ D O.len.a/ len.b//:
An analogous argument shows that one can also compute all of the si ’s in time O.len.a/ len.b//, and in fact, in time O.len.b/2 /. For the reader familiar with the basics of the theory of matrices and determinants, it is instructive to view Theorem 4.3 as follows. For i D 1; : : : ; `, we have ri 0 1 ri 1 D : ri C1 1 qi ri Recursively expanding the right-hand side of this equation, we have
À Mi WD
ri ri C1
D
0 1
1 0 qi 1
1 q1
a : b
This defines the 2 2 matrix Mi for i D 1; : : : ; `. If we also define M0 to be the 2 2 identity matrix, then it is easy to see that for i D 0; : : : ; `, we have si ti Mi D : si C1 ti C1 From these observations, part (i) of Theorem 4.3 is immediate, and part (ii) follows from the fact that Mi is the product of i matrices, each of determinant 1, and the determinant of Mi is evidently si tiC1 ti si C1 . E XERCISE 4.6. In our description of the extended Euclidean algorithm, we made the restriction that the inputs a and b satisfy a b 0. Using this restricted algorithm as a subroutine, give an algorithm that works without any restrictions on its input.
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Euclid’s algorithm
E XERCISE 4.7. Assume notation as in Theorem 4.3. Show that for all i D 2; : : : ; `, we have jti j < jti C1 j and ri 1 jti j < a, and that for all i D 3; : : : ; `, we have jsi j < jsi C1 j and ri 1 jsi j < b. Also show that si ti 0 for i D 0; : : : ; ` C 1. E XERCISE 4.8. Suppose we modify the extended Euclidean algorithm so that it computes balanced remainders; that is, for i D 1; : : : ; `, the values qi and ri C1 are computed so that ri 1 D ri qi C ri C1 and jri j=2 ri C1 < jri j=2. Assume that the si ’s and the ti ’s are computed by the same formula as in Theorem 4.3. Give a detailed analysis of the running time of this algorithm, which should include an analysis of the number of division steps, and the sizes of the si ’s and ti ’s. E XERCISE 4.9. One can extend the binary gcd algorithm discussed in Exercise 4.5 so that in addition to computing d D gcd.a; b/, it also computes s and t such that as Cbt D d . Here is one way to do this (again, we assume that a and b are positive integers): r a; r 0 b; e 0 0 while 2 j r and 2 j r do r aQ r; bQ r 0; s 1; t repeat while 2 j r do r r=2 if 2 j s and 2 j t
r=2; r 0 r 0 =2; e 0; s 0 0; t 0 1
then s else s
eC1
s=2; t t =2 Q .s C b/=2; t
.t
a/=2 Q
r0
while 2 j do r0 r 0 =2 if 2 j s 0 and 2 j t 0 then s 0 s 0 =2; t 0 t 0 =2 0 0 Q else s .s C b/=2; t0 .t 0 if r 0 < r then .r; s; t; r 0 ; s 0 ; t 0 / .r 0 ; s 0 ; t 0 ; r; s; t / 0 0 0 0 0 0 r r r; s s s; t t t 0 until r D 0 d 2e r, output d; s; t
a/=2 Q
Show that this algorithm is correct and runs in time O.k 2 /, where k WD max.len.a/; len.b//. In particular, you should verify that all of the divisions by 2 performed by the algorithm yield integer results. Moreover, show that the outputs s and t are of length O.k/. 4.3 Computing modular inverses and Chinese remaindering One application of the extended Euclidean algorithm is to the problem of computing multiplicative inverses in Zn .
4.3 Computing modular inverses and Chinese remaindering
81
Assume n > 1. Given b 2 f0; : : : ; n 1g, in time O.len.n/2 /, we can determine if b is relatively prime to n, and if so, compute b 1 mod n, as follows. We run the extended Euclidean algorithm on input n; b, obtaining integers d , s, and t, such that d D gcd.n; b/ and ns C bt D d . If d ¤ 1, then b does not have a multiplicative inverse modulo n. Otherwise, if d D 1, then t is a multiplicative inverse of b modulo n; however, it may not lie in the range f0; : : : ; n 1g, as required. By part (vi) of Theorem 4.3, we have jt j n=2 < n; therefore, either t 2 f0; : : : ; n 1g, or t < 0 and t C n 2 f0; : : : ; n 1g. Thus, b 1 mod n is equal to either t or t C n. We also observe that the Chinese remainder theorem (Theorem 2.6) can be made computationally effective: Theorem 4.5 (Effective Chinese remainder theorem). Suppose we are given integers n1 ; : : : ; nk and a1 ; : : : ; ak , where the family fni gkiD1 is pairwise relatively Q prime, and where ni > 1 and 0 ai < ni for i D 1; : : : ; k. Let n WD kiD1 ni . Then in time O.len.n/2 /, we can compute the unique integer a satisfying 0 a < n and a ai .mod ni / for i D 1; : : : ; k. The algorithm is a straightforward implementation of the proof of Theorem 2.6, and runs as follows: Qk n i D1 ni for i 1 to k do ni n=ni , bi ni mod ni , ti bi 1 mod ni , ei ni ti Pk a i D1 ai ei mod n We leave it to the reader to verify the running time bound. E XERCISE 4.10. In this exercise, you are to make the result of Theorem 2.17 effective. Suppose that we are given a positive integer n, two elements ˛; ˇ 2 Zn , and integers ` and m, such that ˛ ` D ˇ m and gcd.`; m/ D 1. Show how to compute
2 Zn such that ˛ D m in time O.len.`/ len.m/ C .len.`/ C len.m// len.n/2 /. E XERCISE 4.11. In this exercise and the next, you are to analyze an “incremental Chinese remaindering algorithm.” Consider the following algorithm, which takes as input integers a1 ; n1 ; a2 ; n2 , such that gcd.n1 ; n2 / D 1; 0 a1 < n1 ; and 0 a2 < n2 : It outputs integers a; n, such that n D n1 n2 ; 0 a < n; a a1 .mod n1 /; and a a2 .mod n2 /; and runs as follows:
82
Euclid’s algorithm
.a2 w n1 1 mod n2 , h a a1 C n1 h, n n1 n2 output a; n
a1 /w mod n2
Show that the algorithm correctly computes a and n as specified, and runs in time O.len.n/ len.n2 //. E XERCISE 4.12. Using the algorithm in the previous exercise as a subroutine, give a simple O.len.n/2 / algorithm that takes as input integers n1 ; : : : ; nk and a1 ; : : : ; ak , where the family fni gkiD1 is pairwise relatively prime, and where ni > 1 and 0 ai < ni for i D 1; : : : ; k, and outputs integers a and n such that Q 0 a < n, n D kiD1 ni , and a ai .mod ni / for i D 1; : : : ; k. The algorithm should be “incremental,” in that it processes the pairs .ni ; ai / one at a time, using time O.len.n/ len.ni // to process each such pair. E XERCISE 4.13. Suppose we are given ˛1 ; : : : ; ˛k 2 Zn . Show how to compute ˛1 1 ; : : : ; ˛k 1 by computing one multiplicative inverse modulo n, and performing fewer than 3k multiplications modulo n. This result is useful, as in practice, if n is several hundred bits long, it may take 10–20 times longer to compute multiplicative inverses modulo n than to multiply modulo n. 4.4 Speeding up algorithms via modular computation An important practical application of the above “computational” version (Theorem 4.5) of the Chinese remainder theorem is a general algorithmic technique that can significantly speed up certain types of computations involving long integers. Instead of trying to describe the technique in some general form, we simply illustrate the technique by means of a specific example: integer matrix multiplication. Suppose we have two m m matrices A and B whose entries are large integers, and we want to compute the product matrix C WD AB. Suppose that for r; s D 1; : : : ; m, the entry of A at row r and column s is ars , and that for s; t D 1; : : : ; m, the entry of B at row s and column t is bst . Then for r; t D 1; : : : ; m, the entry of C at row r and column t is crt , which is given by the usual rule for matrix multiplication: m X crt D ars bst : (4.1) sD1
Suppose further that M is the maximum absolute value of the entries in A and B, so that the entries in C are bounded in absolute value by M 0 WD M 2 m. Let ` WD len.M /. To simplify calculations, let us also assume that m M (this is
4.4 Speeding up algorithms via modular computation
83
reasonable, as we want to consider large values of M , greater than say 2100 , and certainly, we cannot expect to work with 2100 2100 matrices). By just applying the formula (4.1), we can compute the entries of C using m3 multiplications of numbers of length at most `, and m3 additions of numbers of length at most len.M 0 /, where len.M 0 / 2` C len.m/ D O.`/. This yields a running time of O.m3 `2 /:
(4.2)
Using the Chinese remainder theorem, we can actually do much better than this, as follows. For every integer n > 1, and for all r; t D 1; : : : ; m, we have crt
m X
ars bst .mod n/:
(4.3)
sD1 0 such that Moreover, if we compute integers crt 0 crt
m X
ars bst .mod n/
(4.4)
sD1
and if we also have 0 n=2 crt < n=2 and n > 2M 0 ;
(4.5)
then we must have 0 crt D crt :
(4.6)
To see why (4.6) follows from (4.4) and (4.5), observe that (4.3) and (4.4) imply 0 .mod n/, which means that n divides .c 0 /. Then from the that crt crt crt rt bound jcrt j M 0 and from (4.5), we obtain jcrt
0 0 crt j jcrt j C jcrt j M 0 C n=2 < n=2 C n=2 D n:
0 / is a multiple of n, while at the same time So we see that the quantity .crt crt this quantity is strictly less than n in absolute value; hence, this quantity must be zero. That proves (4.6). So from the above discussion, to compute C , it suffices to compute the entries of C modulo n, where we have to make sure that we compute “balanced” remainders in the interval Œ n=2; n=2/, rather than the more usual “least non-negative” remainders. To compute C modulo n, we choose a number of small integers n1 ; : : : ; nk , such Q that the family fni gkiD1 is pairwise relatively prime, and the product n WD kiD1 ni is just a bit larger than 2M 0 . In practice, one would choose the ni ’s to be small primes, and a table of such primes could easily be computed in advance, so that
84
Euclid’s algorithm
all problems up to a given size could be handled. For example, the product of all primes of at most 16 bits is a number that has more than 90; 000 bits. Thus, by simply pre-computing and storing such a table of small primes, we can handle input matrices with quite large entries (up to about 45; 000 bits). Let us assume that we have pre-computed appropriate small primes n1 ; : : : ; nk . Further, we shall assume that addition and multiplication modulo each ni can be done in constant time. This is reasonable from a practical (and theoretical) point of view, since such primes easily “fit” into a machine word, and we can perform modular addition and multiplication using a constant number of built-in machine operations. Finally, we assume that we do not use more ni ’s than are necessary, so that len.n/ D O.`/ and k D O.`/. To compute C , we execute the following steps: 1. For each i D 1; : : : ; k, do the following: .i /
(a) compute aO rs .i / (b) compute bO st
ars mod ni for r; s D 1; : : : ; m, bst mod ni for s; t D 1; : : : ; m,
(c) for r; t D 1; : : : ; m, compute .i / cOrt
m X
.i /
.i / O aO rs bst mod ni :
sD1
2. For each r; t D 1; : : : ; m, apply the Chinese remainder theorem to .1/ .2/ .k/ cOrt ; cOrt ; : : : ; cOrt , obtaining an integer crt , which should be computed as a balanced remainder modulo n, so that n=2 crt < n=2. 3. Output the matrix C , whose entry in row r and column t is crt . Note that in step 2, if our Chinese remainder algorithm happens to be implemented to return an integer a with 0 a < n, we can easily get a balanced remainder by just subtracting n from a if a n=2. The correctness of the above algorithm has already been established. Let us now analyze its running time. The running time of steps 1a and 1b is easily seen to be O.m2 `2 /. Under our assumption about the cost of arithmetic modulo small primes, the cost of step 1c is O.m3 k/, and since k D O.`/, the cost of this step is O.m3 `/. Finally, by Theorem 4.5, the cost of step 2 is O.m2 `2 /. Thus, the total running time of this algorithm is O.m2 `2 C m3 `/: This is a significant improvement over (4.2); for example, if ` m, then the running time of the original algorithm is O.m5 /, while the running time of the modular algorithm is O.m4 /.
4.5 An effective version of Fermat’s two squares theorem
85
E XERCISE 4.14. Apply the ideas above to the problem of computing the product of two polynomials whose coefficients are large integers. First, determine the running time of the “obvious” algorithm for multiplying two such polynomials, then design and analyze a “modular” algorithm. 4.5 An effective version of Fermat’s two squares theorem We proved in Theorem 2.34 (in §2.8.4) that every prime p 1 .mod 4/ can be expressed as a sum of two squares of integers. In this section, we make this theorem computationally effective; that is, we develop an efficient algorithm that takes as input a prime p 1 .mod 4/, and outputs integers r and t such that p D r 2 C t 2 . One essential ingredient in the proof of Theorem 2.34 was Thue’s lemma (Theorem 2.33). Thue’s lemma asserts the existence of certain numbers, and we proved this using the “pigeonhole principle,” which unfortunately does not translate directly into an efficient algorithm to actually find these numbers. However, we can show that these numbers arise as a “natural by-product” of the extended Euclidean algorithm. To make this more precise, let us introduce some notation. For integers a; b, with a b 0, let us define ˚ `C1 EEA.a; b/ WD .ri ; si ; ti / i D0 ; where ri , si , and ti , for i D 0; : : : ; ` C 1, are defined as in Theorem 4.3. Theorem 4.6 (Effective Thue’s lemma). Let n; b; r ; t 2 Z, with 0 b < n and 0 < r n < r t . Further, let EEA.n; b/ D f.ri ; si ; ti /g`C1 i D0 , and let j be the smallest index (among 0; : : : ; ` C 1) such that rj < r . Then setting r WD rj and t WD tj , we have r bt .mod n/; 0 r < r ; and 0 < jtj < t : Proof. Since r0 D n r > 0 D r`C1 , the value of the index j is well defined, and moreover, j 1 and rj 1 r . It follows that jtj j n=rj
1
(by part (v) of Theorem 4.3)
n=r < t (since n < r t ): Since j 1, by part (iv) of Theorem 4.3, we have jtj j jt1 j > 0. Finally, since rj D nsj C btj , we have rj btj .mod n/. What this theorem says is that given n; b; r ; t , to find the desired values r and t , we run the extended Euclidean algorithm on input n; b. This generates a sequence of remainders r0 > r1 > r2 > , where r0 D n and r1 D b. If
86
Euclid’s algorithm
rj is the first remainder in this sequence that falls below r , and if sj and tj are the corresponding numbers computed by the extended Euclidean algorithm, then r WD rj and t WD tj do the job. The other essential ingredient in the proof of Theorem 2.34 was Theorem 2.31, which guarantees the existence of a square root of 1 modulo p when p is a prime congruent to 1 modulo 4. We need an effective version of this result as well. Later, in Chapter 12, we will study the general problem of computing square roots modulo primes. Right now, we develop an algorithm for this special case. Assume we are given a prime p 1 .mod 4/, and we want to compute ˇ 2 Zp such that ˇ 2 D 1. By Theorem 2.32, it suffices to find 2 Zp n.Zp /2 , since then ˇ WD .p 1/=4 (which we can efficiently compute via repeated squaring) satisfies ˇ 2 D 1. While there is no known efficient, deterministic algorithm to find such a , we do know that half the elements of Zp are squares and half are not (see Theorem 2.20), which suggests the following simple “trial and error” strategy to compute ˇ: repeat choose 2 Zp compute ˇ
.p 2 until ˇ D 1 output ˇ
1/=4
As an algorithm, this is not fully specified, as we have to specify a procedure for selecting in each loop iteration. A reasonable approach is to simply choose
at random: this would be an example of a probabilistic algorithm, a notion that we will study in detail in Chapter 9. Let us assume for the moment that this makes sense from a mathematical and algorithmic point of view, so that with each loop iteration, we have a 50% chance of picking a “good” , that is, one in that is not in .Zp /2 . From this, it follows that with high probability, we should find a “good”
in just a few loop iterations (the probability that after k loop iterations we still have not found one is 1=2k ), and that the expected number of loop iterations is just 2. The running time of each loop iteration is dominated by the cost of repeated squaring, which is O.len.p/3 /. It follows that the expected running time of this algorithm (we will make this notion precise in Chapter 9) is O.len.p/3 /. Let us now put all the ingredients together to get an algorithm to find r; t such that p D r 2 C t 2 . 1. Find ˇ 2 Zp such that ˇ 2 D 2. Set b
1, using the above “trial and error” strategy.
rep.ˇ/ (so that ˇ D Œb and b 2 f0; : : : ; p
1g).
3. Run the extended Euclidean algorithm on input p; b to obtain EEA.p; b/,
4.5 An effective version of Fermat’s two squares theorem
87
p and then apply Theorem 4.6 with n WD p, b, and r WD t WD b pc C 1, to obtain the values r and t. 4. Output r; t. When this algorithm terminates, we have r 2 Ct 2 D p, as required: as we argued in the proof of Theorem 2.34, since r bt .mod p/ and b 2 1 .mod p/, it follows that r 2 C t 2 0 .mod p/, and since 0 < r 2 C t 2 < 2p, we must have r 2 C t 2 D p. The (expected) running time of step 1 is O.len.p/3 /. The running p time of step 3 is O.len.p/2 / (note that we can compute b pc in time O.len.p/2 /, using the algorithm in Exercise 3.29). Thus, the total (expected) running time is O.len.p/3 /. Example 4.3. One can check that p WD 1009 is prime and p 1 .mod 4/. Let us express p as a sum of squares using the above algorithm. First, we need to find a square root of 1 modulo p. Let us just try a random number, say 17, and raise this to the power .p 1/=4 D 252. One can calculate that 17252 469 .mod 1009/, and 4692 1 .mod 1009/. So we were lucky with our first try. Now we run the extended Euclidean algorithm on input p D 1009 and b D 469, obtaining the following data: i 0 1 2 3 4 5 6 7 8 9
ri 1009 469 71 43 28 15 13 2 1 0
qi
si 1 0 1 -6 7 -13 20 -33 218 -469
ti 0 2 1 6 -2 1 13 1 -15 1 28 1 -43 6 71 2 -469 1009 p The first rj that falls below the threshold r D b 1009c C 1 D 32 is at j D 4, and so we set r WD 28 and t WD 15. One verifies that r 2 C t 2 D 282 C 152 D 1009 D p. It is natural to ask whether one can solve this problem without resorting to randomization. The answer is “yes” (see §4.8), but the only known deterministic algorithms for this problem are quite impractical (albeit polynomial time). This example illustrates the utility of randomization as an algorithm design technique, which has proved to be invaluable in solving numerous algorithmic problems in number theory; indeed, in §3.4 we already mentioned its use in connection with
88
Euclid’s algorithm
primality testing, and we will explore many other applications as well (after putting the notion of a probabilistic algorithm on firm mathematical ground in Chapter 9). 4.6 Rational reconstruction and applications In the previous section, we saw how to apply the extended Euclidean algorithm to obtain an effective version of Thue’s lemma. Now, Thue’s lemma asserts that for given integers n and b, there exists a pair of integers .r; t / satisfying r bt .mod n/, and contained in a prescribed rectangle, provided the area of the rectangle is large enough, relative to n. In this section, we first prove a corresponding uniqueness theorem, under the assumption that the area of the rectangle is not too large; of course, if r bt .mod n/, then for any non-zero integer q, we also have rq b.tq/, and so we can only hope to guarantee that the ratio r=t is unique. After proving this uniqueness theorem, we show how to make this theorem computationally effective, and then develop several very neat applications. The basic uniqueness statement is as follows: Theorem 4.7. Let n; b; r ; t 2 Z with r 0, t > 0, and n > 2r t . Further, suppose that r; t; r 0 ; t 0 2 Z satisfy r bt .mod n/; jrj r ; 0 < jtj t ;
(4.7)
0
(4.8)
0
0
0
r bt .mod n/; jr j r ; 0 < jt j t : Then r=t D r 0 =t 0 : Proof. Consider the two congruences r bt .mod n/; r 0 bt 0 .mod n/: Subtracting t times the second from t 0 times the first, we obtain rt 0
r 0 t 0 .mod n/:
However, we also have jrt 0
r 0 tj jrjjt 0 j C jr 0 jjt j 2r t < n:
Thus, rt 0 r 0 t is a multiple of n, but less than n in absolute value; the only possibility is that rt 0 r 0 t D 0, which means r=t D r 0 =t 0 . Now suppose that we are given n; b; r ; t 2 Z as in the above theorem; moreover, suppose that there exist r; t 2 Z satisfying (4.7), but that these values are not given to us. Note that under the hypothesis of Theorem 4.7, Thue’s lemma cannot be used to guarantee the existence of such r and t, but in our eventual applications,
89
4.6 Rational reconstruction and applications
we will have other reasons to guarantee this. We would like to find r 0 ; t 0 2 Z satisfying (4.8), and if we do this, then by the theorem, we know that r=t D r 0 =t 0 . We call this the rational reconstruction problem. We can solve this problem efficiently using the extended Euclidean algorithm; indeed, just as in the case of our effective version of Thue’s lemma, the desired values of r 0 and t 0 appear as “natural by-products” of that algorithm. To state the result precisely, let us recall the notation we introduced in the last section: for integers a; b, with a b 0, we defined ˚ `C1 EEA.a; b/ WD .ri ; si ; ti / iD0 ; where ri , si , and ti , for i D 0; : : : :` C 1, are defined as in Theorem 4.3. Theorem 4.8 (Rational reconstruction). Let n; b; r ; t 2 Z with 0 b < n, 0 r < n, and t > 0. Further, suppose that there exist r; s; t 2 Z such that r D ns C bt; jrj r ; and 0 < jt j t : Let EEA.n; b/ D f.ri ; si ; ti /gi`C1 D0 , and let j be the smallest index (among 0; : : : ; ` C 1) such that rj r , and set r 0 WD rj ; s 0 WD sj ; and t 0 WD tj : Then we have: (i) 0 < jt 0 j t ; (ii) if n > 2r t , then for some non-zero integer q, r D r 0 q; s D s 0 q; and t D t 0 q: Proof. Since r0 D n > r 0 D r`C1 , the value of j is well defined, and moreover, j 1, and we have the inequalities 0 rj r < rj
1;
0 < jtj j; jrj r ; and 0 < jtj t ;
rj
1
(4.9)
along with the identities D nsj
1
C btj
1;
(4.10)
rj D nsj C btj ;
(4.11)
r D ns C bt:
(4.12)
We now turn to part (i) of the theorem. Our goal is to prove that jtj j t :
(4.13)
This is the hardest part of the proof. To this end, let WD sj tj
1
sj
1 tj ;
WD .tj
1s
sj
1 t /=;
WD .sj t
tj s/=:
90
Euclid’s algorithm
Since D ˙1, the numbers and are integers; moreover, one may easily verify that they satisfy the equations sj C sj
1
D s;
(4.14)
tj C tj
1
D t:
(4.15)
We now use these identities to prove (4.13). We consider three cases: (i) Suppose D 0. In this case, (4.15) implies tj j t , and since t ¤ 0, this implies jtj j jtj t . (ii) Suppose < 0. In this case, since tj and tj 1 have opposite sign, (4.15) implies jtj D jtj j C jtj 1 j jtj j, and so again, we have jtj j jtj t . (iii) The only remaining possibility is that ¤ 0 and 0. We argue that this is impossible. Adding n times (4.14) to b times (4.15), and using the identities (4.10), (4.11), and (4.12), we obtain rj C rj
1
D r:
If ¤ 0 and and had the same sign, this would imply that jrj D jrj jC jrj 1 j rj 1 , and hence rj 1 jrj r ; however, this contradicts the fact that rj 1 > r . That proves the inequality (4.13). We now turn to the proof of part (ii) of the theorem, which relies critically on this inequality. Assume that n > 2r t :
(4.16)
From (4.11) and (4.12), we have rj btj .mod n/ and r bt .mod n/: Combining this with the inequalities (4.9), (4.13), and (4.16), we see that the hypotheses of Theorem 4.7 are satisfied, and so we may conclude that rtj
rj t D 0:
(4.17)
Subtracting tj times (4.12) from t times (4.11), and using the identity (4.17), we obtain n.stj sj t/ D 0, and hence stj
sj t D 0:
(4.18)
From (4.18), we see that tj j sj t , and since gcd.sj ; tj / D 1, we must have tj j t. So t D tj q for some q, and we must have q ¤ 0 since t ¤ 0. Substituting tj q for t in equations (4.17) and (4.18) yields r D rj q and s D sj q. That proves part (ii) of the theorem. In our applications in this text, we shall only directly use part (ii) of this theorem; however, part (i) has applications as well (see Exercise 4.15).
91
4.6 Rational reconstruction and applications
4.6.1 Application: recovering fractions from their decimal expansions It should be a familiar fact to the reader that every real number has a decimal expansion, and that this decimal expansion is unique, provided one rules out those expansions that end in an infinite run of 9’s (for example, 1=10 D 0:1000 D 0:0999 ). Now suppose that Alice and Bob play a game. Alice thinks of a rational number z WD s=t, where s and t are integers with 0 s < t , and tells Bob some of the high-order digits in the decimal expansion of z. Bob’s goal in the game is to determine z. Can he do this? The answer is “yes,” provided Bob knows an upper bound M on t , and provided Alice gives Bob enough digits. Of course, from grade school, Bob probably remembers that the decimal expansion of z is ultimately periodic, and that given enough digits of z so as to include the periodic part, he can recover z; however, this technique is quite useless in practice, as the length of the period can be huge — ‚.M / in the worst case (see Exercises 4.18–4.20 below). The method we discuss here requires only O.len.M // digits. Suppose Alice gives Bob the high-order k digits of z, for some k 1. That is, if z D 0 : z 1 z2 z3
(4.19)
is the decimal expansion of z, then Alice gives Bob z1 ; : : : ; zk . Now, if 10k is much smaller than M 2 , the number z is not even uniquely determined by these digits, since there are .M 2 / distinct rational numbers of the form s=t, with 0 s < t M (see Exercise 1.33). However, if 10k > 2M 2 , then not only is z uniquely determined by z1 ; : : : ; zk , but using Theorem 4.8, Bob can efficiently compute it. We shall presently describe efficient algorithms for both Alice and Bob, but before doing so, we make a few general observations about the decimal expansion of z. Let e be an arbitrary non-negative integer, and suppose that the decimal expansion of z is as in (4.19). Observe that 10e z D z1 ze : zeC1 zeC2 : It follows that b10e zc D z1 ze : 0 :
(4.20)
Since z D s=t , if we set r WD 10e s mod t , then 10e s D b10e zct C r, and dividing this by t , we have 10e z D b10e zc C r=t , where r=t 2 Œ0; 1/. Therefore, 10e s mod t D 0 : zeC1 zeC2 zeC3 : t
(4.21)
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Euclid’s algorithm
Next, consider Alice. Based on the above discussion, Alice may use the following simple, iterative algorithm to compute z1 ; : : : ; zk , for arbitrary k 1, after she chooses s and t: x1 s for i 1 to k do yi 10xi zi byi =tc xi C1 yi mod t output z1 ; : : : ; zk Correctness follows from the observation that for each i D 1; 2; : : : ; we have xi D 10i 1 s mod t; therefore, by (4.21) with e D i 1, we have xi =t D 0 : zi zi C1 zi C2 ; and consequently, by (4.20) with e D 1 and xi =t in the role of z, we have b10xi =tc D zi . Since each loop iteration takes time O.len.M //, the total time for Alice’s computation is O.k len.M //. Finally, consider Bob. Given the high-order digits z1 ; : : : ; zk of z D s=t, along with the upper bound M on t, he can compute z as follows: Pk k i. 1. Compute n 10k and b i D1 zi 10 2. Run the extended Euclidean algorithm on input n; b to obtain EEA.n; b/, and then apply Theorem 4.8 with n, b, and r WD t WD M , to obtain the values r 0 ; s 0 ; t 0 . 3. Output the rational number s 0 =t 0 . Let us analyze this algorithm, assuming that 10k > 2M 2 . For correctness, we must show that z D s 0 =t 0 . To prove this, observe that by (4.20) with e D k, we have b D bnzc D bns =tc. Moreover, if we set r WD ns mod t, then we have r D ns
bt; 0 r < t r ; 0 < t t ; and n > 2r t :
It follows that the integers s 0 ; t 0 from Theorem 4.8 satisfy s D s 0 q and t D t 0 q for some non-zero integer q. Thus, s=t D s 0 =t 0 , as required. As a bonus, since the extended Euclidean algorithm guarantees that gcd.s 0 ; t 0 / D 1, not only do we obtain z, but we obtain z expressed as a fraction in lowest terms. We leave it to the reader to verify that Bob’s computation may be performed in time O.k 2 /. We conclude that both Alice and Bob can successfully play this game with k chosen so that k D O.len.M //, in which case, their algorithms run in time O.len.M /2 /. Example 4.4. Alice chooses integers s; t, with 0 s < t 1000, and tells Bob the high-order seven digits in the decimal expansion of z WD s=t , from which
4.6 Rational reconstruction and applications
i 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
ri 10000000 7197183 2802817 1591549 1211268 380281 70425 28156 14113 14043 70 43 27 16 11 5 1 0
qi 1 2 1 1 3 5 2 1 1 200 1 1 1 1 2 5
si 1 0 1 -2 3 -5 18 -95 208 -303 511 -102503 103014 -205517 308531 -514048 1336627 -7197183
93
ti 0 1 -1 3 -4 7 -25 132 -289 421 -710 142421 -143131 285552 -428683 714235 -1857153 10000000
Fig. 4.1. Bob’s data from the extended Euclidean algorithm Bob should be able to compute z. Suppose s D 511 and t D 710. Then s=t D 0:7197183098591549 . Bob receives the digits 7; 1; 9; 7; 1; 8; 3, and computes n D 107 and b D 7197183. Running the extended Euclidean algorithm on input n; b, Bob obtains the data in Fig. 4.1. The first rj that meets the threshold r D 1000 is at j D 10, and Bob reads off s 0 D 511 and t 0 D 710, from which he obtains z D s 0 =t 0 D 511=710. Another interesting phenomenon to observe in Fig. 4.1 is that the fractions si =ti are very good approximations to the fraction b=n D 7197183=10000000; indeed, if we compute the error terms b=n C si =ti for i D 1; : : : ; 5, we get (approximately) 0:72;
0:28; 0:053;
0:03; 0:0054:
Thus, we can approximate the “complicated” fraction 7197183=10000000 by the “very simple” fraction 5=7, introducing an absolute error of less than 0:006. Exercise 4.15 explores this “data compression” capability of Euclid’s algorithm in more generality.
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Euclid’s algorithm
4.6.2 Application: Chinese remaindering with errors One interpretation of the Chinese remainder theorem is that if we “encode” an integer a, with 0 a < n, as the sequence .a1 ; : : : ; ak /, where ai D a mod ni for i D 1; : : : ; k, then we can efficiently recover a from this encoding. Here, of course, n D n1 nk , and the family fni gkiD1 is pairwise relatively prime. Suppose that Alice encodes a as .a1 ; : : : ; ak /, and sends this encoding to Bob over some communication network; however, because the network is not perfect, during the transmission of the encoding, some (but hopefully not too many) of the values a1 ; : : : ; ak may be corrupted. The question is, can Bob still efficiently recover the original a from its corrupted encoding? To make the problem more precise, suppose that the original, correct encoding of a is .a1 ; : : : ; ak /, and the corrupted encoding is .b1 ; : : : ; bk /. Let us define G f1; : : : ; kg to be the set of “good” positions i with ai D bi , and B f1; : : : ; kg to be the set of “bad” positions i with ai ¤ bi . We shall assume that jBj `, where ` is some specified parameter. Of course, if Bob hopes to recover a, we need to build some redundancy into the system; that is, we must require that 0 a M for some bound M that is somewhat smaller than n. Now, if Bob knew the location of bad positions, and if the product of the ni ’s at the good positions exceeds M , then Bob could simply discard the errors, and reconstruct a by applying the Chinese remainder theorem to the ai ’s and ni ’s at the good positions. However, in general, Bob will not know a priori the locations of the bad positions, and so this approach will not work. Despite these apparent difficulties, Theorem 4.8 may be used to solve the problem quite easily, as follows. Let P be an upper bound on the product of any ` of the integers n1 ; : : : ; nk (e.g., we could take P to be the product of the ` largest numbers among n1 ; : : : ; nk ). Further, let us assume that n > 2MP 2 . Now, suppose Bob obtains the corrupted encoding .b1 ; : : : ; bk /. Here is what Bob does to recover a: 1. Apply the Chinese remainder theorem, obtaining an integer b, with 0 b < n and b bi .mod ni / for i D 1; : : : ; k. 2. Run the extended Euclidean algorithm on input n; b to obtain EEA.n; b/, and then apply Theorem 4.8 with n, b, r WD MP and t WD P , to obtain values r 0 ; s 0 ; t 0 . 3. If t 0 j r 0 , output the integer r 0 =t 0 ; otherwise, output “error.” We claim that the above procedure outputs a, under our assumption that the Q set B of bad positions is of size at most `. To see this, let t WD i 2B ni . By construction, we have 1 t P . Also, let r WD at, and note that 0 r r and
4.6 Rational reconstruction and applications
95
0 < t t . We claim that r bt .mod n/:
(4.22)
To show that (4.22) holds, it suffices to show that at bt .mod ni /
(4.23)
for all i D 1; : : : ; k. To show this, for each index i we consider two cases: Case 1: i 2 G. In this case, we have ai D bi , and therefore, at ai t bi t bt .mod ni /: Case 2: i 2 B. In this case, we have ni j t , and therefore, at 0 bt .mod ni /: Thus, (4.23) holds for all i D 1; : : : ; k, and so it follows that (4.22) holds. Therefore, the values r 0 ; t 0 obtained from Theorem 4.8 satisfy r at r0 D D D a: 0 t t t One easily checks that both the procedures to encode and decode a value a run in time O.len.n/2 /. The above scheme is an example of an error correcting code, and is actually the integer analog of a Reed–Solomon code. Example 4.5. Suppose we want to encode a 1024-bit message as a sequence of 16bit blocks, so that the above scheme can correct up to 3 corrupted blocks. Without any error correction, we would need just 1024=16 D 64 blocks. However, to correct this many errors, we need a few extra blocks; in fact, 7 will do. Of course, a 1024-bit message can naturally be viewed as an integer a in the set f0; : : : ; 21024 1g, and the ith 16-bit block in the encoding can be viewed as an integer ai in the set f0; : : : ; 216 1g. Setting k WD 71, we select k primes, n1 ; : : : ; nk , each 16-bits in length. In fact, let us choose n1 ; : : : ; nk to be the largest k primes under 216 . If we do this, then the smallest prime among the ni ’s turns out to be 64717, which is greater than 215:98 . We may set M WD 21024 , and since we Q want to correct up to 3 errors, we may set P WD 2316 . Then with n WD i ni , we have n > 27115:98 D 21134:58 > 21121 D 21C1024C616 D 2MP 2 : Thus, with these parameter settings, the above scheme will correct up to 3 corrupted blocks. This comes at a cost of increasing the length of the message from 1024 bits to 71 16 D 1136 bits, an increase of about 11%.
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Euclid’s algorithm
4.6.3 Applications to symbolic algebra Rational reconstruction also has a number of applications in symbolic algebra. We briefly sketch one such application here. Suppose that we want to find the solution v to the equation vA D w; where we are given as input a non-singular square integer matrix A and an integer vector w. The solution vector v will, in general, have rational entries. We stress that we want to compute the exact solution v, and not some floating point approximation to it. Now, we could solve for v directly using Gaussian elimination; however, the intermediate quantities computed by that algorithm would be rational numbers whose numerators and denominators might get quite large, leading to a rather lengthy computation (however, it is possible to show that the overall running time is still polynomial in the input length). Another approach is to compute a solution vector modulo n, where n is a power of a prime that does not divide the determinant of A. Provided n is large enough, one can then recover the solution vector v using rational reconstruction. With this approach, all of the computations can be carried out using arithmetic on integers not too much larger than n, leading to a more efficient algorithm. More of the details of this procedure are developed later, in Exercise 14.18. E XERCISE 4.15. Let n; b 2 Z with 0 b < n, and let EEA.n; b/ D f.ri ; si ; ti /g`C1 i D0 . This exercise develops some key properties of the fractions si =ti as approximations to b=n. For i D 1; : : : ; ` C 1, let i WD b=n C si =ti . (a) Show that i D ri =ti n for i D 1; : : : ; ` C 1. (b) Show that successive i ’s strictly decrease in absolute value, and alternate in sign. (c) Show that ji j < 1=ti2 for i D 1; : : : ; `, and `C1 D 0. (d) Show that for all s; t 2 Z with t ¤ 0, if jb=n s=t j < 1=2t 2 , then s=t D si =ti for some i D 1; : : : ; `C1. Hint: use part (ii) of Theorem 4.8. (e) Consider a fixed index i 2 f2; : : : ; ` C 1g. Show that for all s; t 2 Z, if 0 < jtj jti j and jb=n s=t j ji j, then s=t D si =ti . In this sense, si =ti is the unique, best approximation to b=n among all fractions of denominator at most jti j. Hint: use part (i) of Theorem 4.8. E XERCISE 4.16. Using the decimal approximation 3:141592654, apply Euclid’s algorithm to calculate a rational number of denominator less than 1000 that is within 10 6 of . Illustrate the computation with a table as in Fig. 4.1.
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4.6 Rational reconstruction and applications
E XERCISE 4.17. Show that given integers s; t; k, with 0 s < t, and k > 0, we can compute the kth digit in the decimal expansion of s=t in time O.len.k/ len.t/2 /. WD fzi g1 For the following exercises, we need a definition: a sequence i D1 of elements drawn from some arbitrary set is called .k; `/-periodic for integers k 0 and ` 1 if zi D zi C` for all i > k. is called ultimately periodic if it is .k; `/-periodic for some .k; `/. E XERCISE 4.18. Show that if a sequence is ultimately periodic, then it is .k ; ` /-periodic for some uniquely determined pair .k ; ` / for which the following holds: for every pair .k; `/ such that is .k; `/-periodic, we have k k and ` j `. The value ` in the above exercise is called the period of , and k is called the pre-period of . If its pre-period is zero, then is called purely periodic. E XERCISE 4.19. Let z be a real number whose decimal expansion is an ultimately periodic sequence. Show that z is rational. E XERCISE 4.20. Let z D s=t 2 Q, where s and t are relatively prime integers with 0 s < t. Show that: 0
(a) there exist integers k; k 0 such that 0 k < k 0 and s10k s10k .mod t /; (b) for all integers k; k 0 with 0 k < k 0 , the decimal expansion of z is .k; k 0 0 k/-periodic if and only if s10k s10k .mod t /; (c) if gcd.10; t/ D 1, then the decimal expansion of z is purely periodic with period equal to the multiplicative order of 10 modulo t; (d) more generally, if k is the smallest non-negative integer such that 10 and t 0 WD t= gcd.10k ; t / are relatively prime, then the decimal expansion of z is ultimately periodic with pre-period k and period equal to the multiplicative order of 10 modulo t 0 . A famous conjecture of Artin postulates that for every integer d , not equal to 1 or to the square of an integer, there are infinitely many primes t such that d has multiplicative order t 1 modulo t. If Artin’s conjecture is true, then by part (c) of the previous exercise, there are infinitely many primes t such that the decimal expansion of s=t, for every s with 0 < s < t, is a purely periodic sequence of period t 1. In light of these observations, the “grade school” method of computing a fraction from its decimal expansion using the period is hopelessly impractical.
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Euclid’s algorithm
4.7 The RSA cryptosystem One of the more exciting uses of number theory in recent decades is its application to cryptography. In this section, we give a brief overview of the RSA cryptosystem, named after its inventors Rivest, Shamir, and Adleman. At this point in the text, we already have the concepts and tools at our disposal necessary to understand the basic operation of this system, even though a full understanding of the system will require other ideas that will developed later in the text. Suppose that Alice wants to send a secret message to Bob over an insecure network. An adversary may be able to eavesdrop on the network, and so sending the message “in the clear” is not an option. Using older, more traditional cryptographic techniques would require that Alice and Bob share a secret key between them; however, this creates the problem of securely generating such a shared secret. The RSA cryptosystem is an example of a public key cryptosystem. To use the system, Bob simply places a “public key” in the equivalent of an electronic telephone book, while keeping a corresponding “private key” secret. To send a secret message to Bob, Alice obtains Bob’s public key from the telephone book, and uses this to encrypt her message. Upon receipt of the encrypted message, Bob uses his private key to decrypt it, obtaining the original message. Here is how the RSA cryptosystem works. To generate a public key/private key pair, Bob generates two very large, random primes p and q, with p ¤ q. To be secure, p and q should be quite large; in practice, they are chosen to be around 512 bits in length. Efficient algorithms for generating such primes exist, and we shall discuss them in detail later in the text (that there are sufficiently many primes of a given bit length will be discussed in Chapter 5; algorithms for generating them will be discussed at a high level in §9.4, and in greater detail in Chapter 10). Next, Bob computes n WD pq. Bob also selects an integer e > 1 such that gcd.e; .n// D 1. Here, .n/ D .p 1/.q 1/. Finally, Bob computes d WD e 1 mod .n/, using the extended Euclidean algorithm. The public key is the pair .n; e/, and the private key is the pair .n; d /. The integer e is called the “encryption exponent” and d is called the “decryption exponent.” In practice, the integers n and d are about 1024 bits in length, while e is usually significantly shorter. After Bob publishes his public key .n; e/, Alice may send a secret message to Bob as follows. Suppose that a message is encoded in some canonical way as a number between 0 and n 1 — we can always interpret a bit string of length less than len.n/ as such a number. Thus, we may assume that a message is an element ˛ of Zn . To encrypt the message ˛, Alice simply computes ˇ WD ˛ e using repeated squaring. The encrypted message is ˇ. When Bob receives ˇ, he computes WD ˇ d , and interprets as a message. The most basic requirement of any encryption scheme is that decryption should
99
4.7 The RSA cryptosystem
“undo” encryption. In this case, this means that for all ˛ 2 Zn , we should have .˛ e /d D ˛:
(4.24)
If ˛ 2 Zn , then this is clearly the case, since we have ed D 1 C .n/k for some positive integer k, and hence by Euler’s theorem (Theorem 2.13), we have .˛ e /d D ˛ ed D ˛ 1C .n/k D ˛ ˛ .n/k D ˛: To argue that (4.24) holds in general, let ˛ be an arbitrary element of Zn , and suppose ˛ D Œan . If a 0 .mod p/, then trivially aed 0 .mod p/; otherwise, aed a1C .n/k a a .n/k a .mod p/; where the last congruence follows from the fact that .n/k is a multiple of p 1, which is a multiple of the multiplicative order of a modulo p (again by Euler’s theorem). Thus, we have shown that aed a .mod p/. The same argument shows that aed a .mod q/, and these two congruences together imply that aed a .mod n/. Thus, we have shown that equation (4.24) holds for all ˛ 2 Zn . Of course, the interesting question about the RSA cryptosystem is whether or not it really is secure. Now, if an adversary, given only the public key .n; e/, were able to factor n, then he could easily compute the decryption exponent d himself using the same algorithm used by Bob. It is widely believed that factoring n is computationally infeasible, for sufficiently large n, and so this line of attack is ineffective, barring a breakthrough in factorization algorithms. Indeed, while trying to factor n by brute-force search is clearly infeasible, there are much faster algorithms, but even these are not fast enough to pose a serious threat to the security of the RSA cryptosystem. We shall discuss some of these faster algorithms in some detail later in the text (in Chapter 15). Can one break the RSA cryptosystem without factoring n? For example, it is natural to ask whether one can compute the decryption exponent d without having to go to the trouble of factoring n. It turns out that the answer to this question is “no”: if one could compute the decryption exponent d , then ed 1 would be a multiple of .n/, and as we shall see later in §10.4, given any multiple of .n/, we can easily factor n. Thus, computing the decryption exponent is equivalent to factoring n, and so this line of attack is also ineffective. But there still could be other lines of attack. For example, even if we assume that factoring large numbers is infeasible, this is not enough to guarantee that for a given encrypted message ˇ, the adversary is unable to compute ˇ d (although nobody actually knows how to do this without first factoring n). The reader should be warned that the proper notion of security for an encryption scheme is quite subtle, and a detailed discussion of this is well beyond the scope
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Euclid’s algorithm
of this text. Indeed, the simple version of RSA presented here suffers from a number of security problems (because of this, actual implementations of public-key encryption schemes based on RSA are somewhat more complicated). We mention one such problem here (others are examined in some of the exercises below). Suppose an eavesdropping adversary knows that Alice will send one of a few, known, candidate messages. For example, an adversary may know that Alice’s message is either “let’s meet today” or “let’s meet tomorrow.” In this case, the adversary can encrypt for himself each of the candidate messages, intercept Alice’s actual encrypted message, and then by simply comparing encryptions, the adversary can determine which particular message Alice encrypted. This type of attack works simply because the encryption algorithm is deterministic, and in fact, any deterministic encryption algorithm will be vulnerable to this type of attack. To avoid this type of attack, one must use a probabilistic encryption algorithm. In the case of the RSA cryptosystem, this is often achieved by padding the message with some random bits before encrypting it (but even this must be done carefully). E XERCISE 4.21. This exercise develops an algorithm for speeding up RSA decryption. Suppose that we are given two distinct `-bit primes, p and q, an element ˇ 2 Zn , where n WD pq, and an integer d , where 1 < d < .n/. Using the algorithm from Exercise 3.35, we can compute ˇ d at a cost of essentially 2` squarings in Zn . Show how this can be improved, making use of the factorization of n, so that the total cost is essentially that of ` squarings in Zp and ` squarings in Zq , leading to a roughly four-fold speed-up in the running time. E XERCISE 4.22. Alice submits a bid to an auction, and so that other bidders cannot see her bid, she encrypts it under the public key of the auction service. Suppose that the auction service provides a public key for an RSA encryption scheme, with a modulus n. Assume that bids are encoded simply as integers between 0 and n 1 prior to encryption. Also, assume that Alice submits a bid that is a “round number,” which in this case means that her bid is a number that is divisible by 10. Show how an eavesdropper can submit an encryption of a bid that exceeds Alice’s bid by 10%, without even knowing what Alice’s bid is. In particular, your attack should work even if the space of possible bids is very large. E XERCISE 4.23. To speed up RSA encryption, one may choose a very small encryption exponent. This exercise develops a “small encryption exponent attack” on RSA. Suppose Bob, Bill, and Betty have RSA public keys with moduli n1 , n2 , and n3 , and all three use encryption exponent 3. Assume that fni g3iD1 is pairwise relatively prime. Suppose that Alice sends an encryption of the same message to Bob, Bill, and Betty — that is, Alice encodes her message as an integer
4.8 Notes
101
a, with 0 a < minfn1 ; n2 ; n3 g, and computes the three encrypted messages ˇi WD Œa3 ni , for i D 1; : : : ; 3. Show how to recover Alice’s message from these three encrypted messages. E XERCISE 4.24. To speed up RSA decryption, one might choose a small decryption exponent, and then derive the encryption exponent from this. This exercise develops a “small decryption exponent attack” on RSA. Suppose n D pq, where p and q are distinct primes with len.p/ D len.q/. Let d and e be integers such that 1 < d < .n/, 1 < e < .n/, and de 1 .mod .n//. Further, assume that d < n1=4 =3: Show how to efficiently compute d , given n and e. Hint: since ed 1 .mod .n//, it follows that ed D 1 C .n/k for an integer k with 0 < k < d ; let r WD nk ed , and show that jrj < n3=4 ; next, show how to recover d (along with r and k) using Theorem 4.8. 4.8 Notes The Euclidean algorithm as we have presented it here is not the fastest known algorithm for computing greatest common divisors. The asymptotically fastest known algorithm for computing the greatest common divisor of two numbers of bit length at most ` runs in time O.` len.`// on a RAM. This algorithm is due to Schönhage [83]. The same algorithm leads to Boolean circuits of size O.` len.`/2 len.len.`///, which using Fürer’s result [37], can be reduced to O.` len.`/2 2O.log n/ /. The same complexity results also hold for the extended Euclidean algorithm, as well as for Chinese remaindering, Thue’s lemma, and rational reconstruction. Experience suggests that such fast algorithms for greatest common divisors are not of much practical value, unless the integers involved are very large — at least several tens of thousands of bits in length. The extra “log” factor and the rather large multiplicative constants seem to slow things down too much. The binary gcd algorithm (Exercise 4.5) is due to Stein [98]. The extended binary gcd algorithm (Exercise 4.9) was first described by Knuth [55], who attributes it to M. Penk. Our formulation of both of these algorithms closely follows that of Menezes, van Oorschot, and Vanstone [64]. Experience suggests that the binary gcd algorithm is faster in practice than Euclid’s algorithm. Schoof [85] presents (among other things) a deterministic, polynomial-time algorithm that computes a square root of 1 modulo p for any given prime p 1 .mod 4/. If we use this algorithm in §4.5, we get a deterministic, polynomial-time algorithm to compute integers r and t such that p D r 2 C t 2 . Our Theorem 4.8 is a generalization of one stated in Wang, Guy, and Davenport [101]. One can generalize Theorem 4.8 using the theory of continued fractions. With this, one can generalize Exercise 4.15 to deal with rational approximations to
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Euclid’s algorithm
irrational numbers. More on this can be found, for example, in the book by Hardy and Wright [45]. The application of Euclid’s algorithm to computing a rational number from the first digits of its decimal expansion was observed by Blum, Blum, and Shub [17], where they considered the possibility of using such sequences of digits as a pseudorandom number generator — the conclusion, of course, is that this is not such a good idea. The RSA cryptosystem was invented by Rivest, Shamir, and Adleman [80]. There is a vast literature on cryptography. One starting point is the book by Menezes, van Oorschot, and Vanstone [64]. The attack in Exercise 4.24 is due to Wiener [108]; this attack was recently strengthened by Boneh and Durfee [19].
5 The distribution of primes
This chapter concerns itself with the question: how many primes are there? In Chapter 1, we proved that there are infinitely many primes; however, we are interested in a more quantitative answer to this question; that is, we want to know how “dense” the prime numbers are. This chapter has a bit more of an “analytical” flavor than other chapters in this text. However, we shall not make use of any mathematics beyond that of elementary calculus. 5.1 Chebyshev’s theorem on the density of primes The natural way of measuring the density of primes is to count the number of primes up to a bound x, where x is a real number. For each real number x 0, we define .x/ to be the number of primes up to (and including) x. For example, .1/ D 0, .2/ D 1, and .7:5/ D 4. The function is an example of a “step function,” that is, a function that changes values only at a discrete set of points. It might seem more natural to define only on the integers, but it is the tradition to define it over the real numbers (and there are some technical benefits in doing so). Let us first take a look at some values of .x/. Table 5.1 shows values of .x/ for x D 103i and i D 1; : : : ; 6. The third column of this table shows the value of x=.x/ (to five decimal places). One can see that the differences between successive rows of this third column are roughly the same — about 6:9 — which suggests that the function x=.x/ grows logarithmically in x. Indeed, as log.103 / 6:9, it would not be unreasonable to guess that x=.x/ log x, or equivalently, .x/ x= log x. (As discussed in the Preliminaries, log x denotes the natural logarithm of x.) The following theorem is a first — and important — step towards making the above guesswork more rigorous:
103
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The distribution of primes
Table 5.1. Some values of .x/ x .x/ x=.x/ 103 168 5.95238 106 78498 12.73918 109 50847534 19.66664 1012 37607912018 26.59015 1015 29844570422669 33.50693 1018 24739954287740860 40.42045 Theorem 5.1 (Chebyshev’s theorem). We have .x/ D ‚.x= log x/: It is not too difficult to prove this theorem, which we now proceed to do in several steps. We begin with a simple lower bound on binomial coefficients (see §A2): Lemma 5.2. If m is a positive integer, then ! 2m 22m =2m: m Proof. As 2m m is the largest binomial coefficient in the binomial expansion of .1 C 1/2m , we have ! ! ! ! 2m 2m X X1 2m 2m 2m 2m 2m 2 D D 1C C 1 2 C .2m 1/ 2m : i i m m i D0
i D1
Next, recalling that p .n/ denotes the power to which a prime p divides an integer n, we make with the following observation: Lemma 5.3. Let n be a positive integer. For every prime p, we have X p .nŠ/ D bn=p k c: k1
Proof. For positive integers j; k, define dj k WD 1 if p k j j , and dj k WD 0, otherP wise. Observe that p .j / D k1 dj k (this sum is actually finite, since dj k D 0 for all sufficiently large k). So we have p .nŠ/ D
n X j D1
p .j / D
n X X j D1 k1
dj k D
n XX k1 j D1
dj k :
5.1 Chebyshev’s theorem on the density of primes
105
Pn
Finally, note that j D1 dj k is equal to the number of multiples of p k among the integers 1; : : : ; n, which by Exercise 1.3 is equal to bn=p k c. The following theorem gives a lower bound on .x/. Theorem 5.4. .n/ 21 .log 2/n= log n for every integer n 2. Proof. For positive integer m, consider the binomial coefficient ! 2m .2m/Š N WD D : m .mŠ/2 It is clear that N is divisible only by primes p up to 2m. Applying Lemma 5.3 to the identity N D .2m/Š=.mŠ/2 , we have X p .N / D .b2m=p k c 2bm=p k c/: k1
Each term in this sum is either 0 or 1 (see Exercise 1.4), and for k > log.2m/= log p, each term is zero. Thus, p .N / log.2m/= log p. So we have X log.2m/ .2m/ log.2m/ D log p log p p2m X p .N / log p D log N; p2m
where the summations are over the primes p up to 2m. By Lemma 5.2, we have N 22m =2m 2m , and hence .2m/ log.2m/ m log 2 D 12 .log 2/.2m/: That proves the theorem for even n. Now consider odd n 3, so n D 2m 1 for some m 2. It is easily verified that the function x= log x is increasing for x 3; therefore, .2m
1/ D .2m/ 21 .log 2/.2m/= log.2m/ 12 .log 2/.2m
1/= log.2m
1/:
That proves the theorem for odd n. As a consequence of the above theorem, we have .x/ D .x= log x/ for real x ! 1. Indeed, for every real number x 2, setting c WD 21 .log 2/, we have .x/ D .bxc/ cbxc= logbxc c.x
1/= log x D .x= log x/:
106
The distribution of primes
To obtain a corresponding upper bound for .x/, we introduce an auxiliary function, called Chebyshev’s theta function: X #.x/ WD log p; px
where the sum is over all primes p up to x. Chebyshev’s theta function is an example of a summation over primes, and in this chapter, we will be considering a number of functions that are defined in terms of sums or products over primes. To avoid excessive tedium, we adopt the usual convention used by number theorists: if not explicitly stated, summations and products over the variable p are always understood to be over primes. For example, we P may write .x/ D px 1. Theorem 5.5. We have #.x/ D ‚..x/ log x/: Proof. On the one hand, we have X X #.x/ D log p log x 1 D .x/ log x: px
px
On the other hand, we have X X #.x/ D log p px
D
1 2
log p
1 2
log x
x 1=2
log x .x/
.x 1=2 / D
X
1
x 1=2
1
.x 1=2 /=.x/ .x/ log x:
It will therefore suffice to show that .x 1=2 /=.x/ D o.1/. Certainly, .x 1=2 / x 1=2 . Moreover, by the previous theorem, .x/ D .x= log x/. Therefore, .x 1=2 /=.x/ D O.log x=x 1=2 / D o.1/; and the theorem follows. Theorem 5.6. #.x/ < 2.log 2/x for every real number x 1. Proof. It suffices to prove that #.n/ < 2.log 2/n for every positive integer n, since then #.x/ D #.bxc/ < 2.log 2/bxc 2.log 2/x. We prove this by induction on n. For n D 1 and n D 2, this is clear, so assume n > 2. If n is even, then using the induction hypothesis for n 1, we have #.n/ D #.n
1/ < 2.log 2/.n
1/ < 2.log 2/n:
Now consider the case where n is odd. Write n D 2m C 1, where m is a positive
5.2 Bertrand’s postulate
107
integer, and consider the binomial coefficient ! 2m C 1 .2m C 1/Š M WD : D mŠ.m C 1/Š m Observe that M is divisible by all primes p with m C 1 < p 2m C 1. Moreover, as M occurs twice in the binomial expansion of .1 C 1/2mC1 , we have M < 22mC1 =2 D 22m : It follows that X #.2m C 1/ #.m C 1/ D log p log M < 2.log 2/m: mC1
Using this, and the induction hypothesis for m C 1, we obtain #.n/ D #.2m C 1/
#.m C 1/ C #.m C 1/
< 2.log 2/m C 2.log 2/.m C 1/ D 2.log 2/n: Another way of stating the above theorem is: Y p < 4x : px
Theorem 5.1 follows immediately from Theorems 5.4, 5.5 and 5.6. Note that we have also proved: Theorem 5.7. We have #.x/ D ‚.x/: E XERCISE 5.1. For each positive integer n, let pn denote the nth prime. Show that pn D ‚.n log n/. E XERCISE 5.2. For each positive integer n, let !.n/ denote the number of distinct primes dividing n. Show that !.n/ D O.log n= log log n/. P E XERCISE 5.3. Show that px 1= log p D ‚.x=.log x/2 /. 5.2 Bertrand’s postulate Suppose we want to know how many primes there are of a given bit length, or more generally, how many primes there are between m and 2m for a given positive integer m. Neither the statement, nor our proof, of Chebyshev’s theorem imply that there are any primes between m and 2m, let alone a useful density estimate of such primes. Bertrand’s postulate is the assertion that for every positive integer m, there exists a prime between m and 2m. We shall in fact prove a stronger result, namely,
108
The distribution of primes
that not only is there one prime, but the number of primes between m and 2m is .m= log m/. Theorem 5.8 (Bertrand’s postulate). For every positive integer m, we have m .2m/ .m/ > : 3 log.2m/ The proof uses Theorem 5.6, along with a more careful re-working of the proof of Theorem 5.4. The theorem is clearly true for m 2, sowe may assume that m 3. As in the proof of the Theorem 5.4, define N WD 2m m , and recall that N is divisible only by primes less than 2m, and that we have the identity X p .N / D .b2m=p k c 2bm=p k c/; (5.1) k1
where each term in the sum is either 0 or 1. We can characterize the values p .N / a bit more precisely, as follows: Lemma 5.9. Let m 3 and N WD 2m m . For all primes p, we have p p .N / 2m; p if p > 2m, then p .N / 1;
(5.3)
if 2m=3 < p m, then p .N / D 0;
(5.4)
if m < p < 2m, then p .N / D 1.
(5.5)
(5.2)
Proof. For (5.2), all terms with k > log.2m/= log p in (5.1) vanish, and hence p .N / log.2m/= log p, from which it follows that p p .N / 2m. (5.3) follows immediately from (5.2). For (5.4), if 2m=3 < p m, then 2m=p < 3, and we must also have p 3, since p D 2 implies m < 3. We have p 2 > p.2m=3/ D 2m.p=3/ 2m, and hence all terms with k > 1 in (5.1) vanish. The term with k D 1 also vanishes, since 1 m=p < 3=2, from which it follows that 2 2m=p < 3, and hence bm=pc D 1 and b2m=pc D 2. For (5.5), if m < p < 2m, it follows that 1 < 2m=p < 2, so b2m=pc D 1. Also, m=p < 1, so bm=pc D 0. It follows that the term with k D 1 in (5.1) is 1, and it is clear that 2m=p k < 1 for all k > 1, and so all the other terms vanish. We now have the necessary technical ingredients to prove Theorem 5.8. Define Y Pm WD p; m
and define Qm so that N D Qm Pm :
109
5.3 Mertens’ theorem
By (5.4) and (5.5), we see that Y
Qm D
p p .N / :
p2m=3
p 2m, so there are at most Moreover, by (5.3), .N / > 1 for at most those p p p 2m such primes, and by (5.2), the contribution of each such prime to the above product is at most 2m. Combining this with Theorem 5.6, we obtain p 2m
Qm < .2m/
42m=3 :
We now apply Lemma 5.2, obtaining Pm D NQm1 22m .2m/
1
Qm1 > 4m=3 .2m/
p .1C 2m/
:
It follows that .2m/
p m log 4 .1 C 2m/ 3 log.2m/ p m m.log 4 1/ D C .1 C 2m/: 3 log.2m/ 3 log.2m/
.m/ log Pm = log.2m/ >
Clearly, for all sufficiently large m, we have p m.log 4 1/ > 1 C 2m: 3 log.2m/
(5.6)
That proves Theorem 5.8 for all sufficiently large m. Moreover, a simple calculation shows that (5.6) holds for all m 13; 000, and one can verify by brute force (with the aid of a computer) that the theorem holds for m < 13; 000. 5.3 Mertens’ theorem Our next goal is to prove the following theorem, which turns out to have a number of applications. Theorem 5.10. We have X 1 D log log x C O.1/: p px The proof of this theorem, while not difficult, is a bit technical, and we proceed in several steps. Theorem 5.11. We have X log p D log x C O.1/: p px
110
The distribution of primes
Proof. Let n WD bxc. The idea of the proof is to estimate log.nŠ/ in two different ways. By Lemma 5.3, we have XX X XX log.nŠ/ D bn=p k c log p D bn=pc log p C bn=p k c log p: pn k1
pn
k2 pn
We next show that the last sum is O.n/. We have X X X X log p bn=p k c n log p p pn
pn
k2
k
k2
X log p X log p 1 D n Dn p 2 1 1=p p.p 1/ pn pn n
X k2
log k D O.n/: k.k 1/
Thus, we have shown that log.nŠ/ D
X
bn=pc log p C O.n/:
pn
Since bn=pc D n=p C O.1/, applying Theorem 5.6 (and Exercise 3.12), we obtain X X X log p log.nŠ/ D .n=p/ log p C O. log p/ C O.n/ D n C O.n/: (5.7) p pn pn pn We can also estimate log.nŠ/ by estimating a sum by an integral (see §A5): Z n n X log.nŠ/ D log k D log t dt C O.log n/ D n log n n C O.log n/: (5.8) kD1
1
Combining (5.7) and (5.8), and noting that log x log n D o.1/ (see Exercise 3.11), we obtain X log p D log n C O.1/ D log x C O.1/; p px which proves the theorem. We shall also need the following theorem, which is a very useful tool in its own right; it is essentially a discrete variant of “integration by parts.” Theorem 5.12 (Abel’s identity). Let fci g1 be a sequence of real numbers, and iDk for each real number t, define X C.t / WD ci : ki t
111
5.3 Mertens’ theorem
Further, suppose that f .t / is a function with a continuous derivative f 0 .t / on the interval Œk; x, where x is a real number, with x k. Then Z x X ci f .i / D C.x/f .x/ C.t /f 0 .t / dt: k
ki x
Note that since C.t / is a step function, the integrand C.t /f 0 .t / is piece-wise continuous on Œk; x, and hence the integral is well defined (see §A4). Proof. Let n WD bxc. We have n X
ci f .i/ D C.k/f .k/ C ŒC.k C 1/
i Dk
C ŒC.n/
C.n
D C.k/Œf .k/
C.k/f .k C 1/ C
1/f .n/
f .k C 1/ C C C.n
1/Œf .n
1/
f .n/
1/Œf .n
1/
f .n/
C C.n/f .n/ D C.k/Œf .k/
f .k C 1/ C C C.n
C C.n/Œf .n/ Observe that for i D k; : : : ; n so
f .x/ C C.x/f .x/:
1, we have C.t / D C.i / for all t 2 Œi; i C 1/, and i C1
Z C.i/Œf .i /
f .i C 1/ D
C.t /f 0 .t / dtI
i
likewise, Z C.n/Œf .n/
f .x/ D
x
C.t /f 0 .t / dt;
n
from which the theorem directly follows. Proof of Theorem 5.10. For i 2, set .log i /= i ci WD 0
if i is prime, otherwise.
By Theorem 5.11, we have C.t / WD
X 2i t
ci D
X log p pt
p
D log t C R.t /;
where R.t/ D O.1/. Applying Theorem 5.12 with f .t / WD 1= log t (and using
112
The distribution of primes
Exercise 3.13), we obtain Z x X 1 X C.t / C.x/ D C dt ci f .i / D p log x t .log t /2 2 px 2i x Z x Z x R.x/ dt R.t / D1C C C dt 2 log x 2 t log t 2 t .log t / D 1 C O.1= log x/ C .log log x log log 2/ C O.1/ D log log x C O.1/: Using Theorem 5.10, we can easily show the following: Theorem 5.13 (Mertens’ theorem). We have Y .1 1=p/ D ‚.1= log x/: px
Proof. Using parts (i) and (iii) of §A1, for any fixed prime p, we have 1 1 C log.1 p2 p
1=p/ 0:
(5.9)
Moreover, since X 1 X 1 < 1; p2 i2 px i 2
summing the inequality (5.9) over all primes p x yields X 1 C C log g.x/ 0; p px Q where C is a positive constant, and g.x/ WD px .1 1=p/. From this, and from Theorem 5.10, we obtain log g.x/ D log log x C O.1/, which implies that g.x/ D ‚.1= log x/ (see Exercise 3.11) That proves the theorem. E XERCISE 5.4. For each positive integer k, let Pk denote the product of the first k primes. Show that .Pk / D ‚.Pk = log log Pk /. E XERCISE 5.5. The previous exercise showed that .n/ could be as small as (about) n= log log n for infinitely many n. Show that this is the “worst case,” in the sense that .n/ D .n= log log n/ as n ! 1. E XERCISE 5.6. Show that for every positive integer constant k, Z x dt x x D CO : k .log x/k .log x/kC1 2 .log t / This fact may be useful in some of the following exercises.
5.3 Mertens’ theorem
113
E XERCISE 5.7. Use Chebyshev’s theorem and Abel’s identity to prove a stronger version of Theorem 5.5: #.x/ D .x/ log x C O.x= log x/: E XERCISE 5.8. Use Chebyshev’s theorem and Abel’s identity to show that X 1 .x/ D C O.x=.log x/3 /: log p log x px E XERCISE 5.9. Show that Y
.1
2=p/ D ‚.1=.log x/2 /:
2
E XERCISE 5.10. Show that if .x/ cx= log x for some constant c, then we must have c D 1. P E XERCISE 5.11. Strengthen Theorem 5.10: show that px 1=p D log log x C A C o.1/ for some constant A. (Note: A 0:261497212847643.) E XERCISE 5.12. Use the result from the previous exercise to strengthen Mertens’ Q theorem: show that px .1 1=p/ B1 =.log x/ for some constant B1 . (Note: B1 0:561459483566885.) E XERCISE 5.13. Strengthen the result of Exercise 5.9: show that Y .1 2=p/ B2 =.log x/2 2
for some constant B2 . (Note: B2 0:832429065662.) E XERCISE 5.14. Use Abel’s identity to derive Euler’s summation formula: if f .t / has a continuous derivative f 0 .t / on the interval Œa; b, where a and b are integers, then Z b Z b b X f .i/ f .t /dt D f .a/ C .t bt c/f 0 .t /dt: i Da
a
a
E XERCISE 5.15. Use Euler’s summation formula (previous exercise) to show that n C 21 log n C O.1/; p and from this, conclude that nŠ D ‚..n=e/n n/. This ispa weak form of Stirling’s approximation; a sharper form states that nŠ .n=e/n 2 n. log.nŠ/ D n log n
E XERCISE 5.16. Use Stirling’s approximation (previous exercise) to show that ! p 2m D ‚.4m = m/: m
114
The distribution of primes
5.4 The sieve of Eratosthenes As an application of Theorem 5.10, consider the sieve of Eratosthenes. This is an algorithm that generates all the primes up to a given bound n. It uses an array AŒ2 : : : n, and runs as follows. for k for k
2 to n do AŒk 1 p 2 to b nc do if AŒk D 1 then i 2k; while i n do f AŒi
0; i
i Ck g
When the algorithm finishes, we have AŒk D 1 if and only if k is prime, for k D 2; : : : ; n. This can easily be proven using the fact (see Exercise 1.2) that a composite number k between 2 and n must be divisible by a prime that is at most p n, and by proving by induction on k that at the beginning of each iteration of the main loop, AŒi D 0 if and only if i is divisible by a prime less than k, for i D k; : : : ; n. We leave the details of this to the reader. We are more interested in the running time of the algorithm. To analyze the running time, we assume that all arithmetic operations take constant time; this is reasonable, since all the numbers computed are used as array indices and thus should fit in single machine words. Therefore, we can assume that built-in arithmetic instructions are used for operating on such numbers. Every time we execute the inner loop of the algorithm, we perform O.n=k/ steps to clear the entries of A indexed by multiples of k. Pessimistically, then, we could bound the total running time by O.n T .n//, where X T .n/ WD 1=k: p k n
Estimating the sum by an integral (see §A5), we have p b nc
T .n/ D
X kD1
Z 1=k D
1
p b nc
dy 1 C O.1/ log n: y 2
This implies a O.n len.n// bound on the running time of the algorithm. However, this rather crude analysis ignores the fact that the inner loop is executed only for prime values of k; taking this fact into account, we see that the running time is O.n T1 .n//, where X T1 .n/ WD 1=p: p p n
5.5 The prime number theorem . . . and beyond
115
By Theorem 5.10, T1 .n/ D log log n C O.1/, which implies a O.n len.len.n/// bound on the running time of the algorithm. This is a substantial improvement over the above, rather crude analysis. E XERCISE 5.17. Give a detailed proof of the correctness of the above algorithm. E XERCISE 5.18. One drawback of the above algorithm is its use of space: it requires an array of size n. Show how to modify the algorithm, without substantially increasing its running time, so that one can enumerate all the primes up to n, using p an auxiliary array of size just O. n/. E XERCISE 5.19. Design and analyze an algorithm that on input n outputs the table of values .k/ for k D 1; : : : ; n, where .k/ is the number of positive divisors of k. Your algorithm should run in time O.n len.n//. 5.5 The prime number theorem . . . and beyond In this section, we survey a number of theorems and conjectures related to the distribution of primes. This is a vast area of mathematical research, with a number of very deep results. We shall be stating a number of theorems from the literature in this section without proof; while our intent is to keep the text as self contained as possible, and to avoid degenerating into “mathematical tourism,” it nevertheless is a good idea to occasionally have a somewhat broader perspective. In the following chapters, we shall not make any critical use of the theorems in this section. 5.5.1 The prime number theorem The main theorem in the theory of the density of primes is the following. Theorem 5.14 (Prime number theorem). We have .x/ x= log x: Proof. Literature—see §5.6. As we saw in Exercise 5.10, if .x/=.x= log x/ tends to a limit as x ! 1, then the limit must be 1, so in fact the hard part of proving the prime number theorem is to show that .x/=.x= log x/ does indeed tend to some limit. E XERCISE 5.20. Using the prime number theorem, show that #.x/ x. E XERCISE 5.21. Using the prime number theorem, show that pn n log n, where pn denotes the nth prime.
116
The distribution of primes
Table 5.2. Values of .x/, li.x/, and x= log x x 103 106 109 1012 1015 1018
.x/ 168 78498 50847534 37607912018 29844570422669 24739954287740860
li.x/ 176.6 78626.5 50849233.9 37607950279.8 29844571475286.5 24739954309690414.0
x= log x 144.8 72382.4 48254942.4 36191206825.3 28952965460216.8 24127471216847323.8
E XERCISE 5.22. Using the prime number theorem, show that Bertrand’s postulate can be strengthened (asymptotically) as follows: for every > 0, there exist positive constants c and x0 , such that for all x x0 , we have x ..1 C /x/ .x/ c : log x 5.5.2 The error term in the prime number theorem The prime number theorem says that j.x/
x= log xj ı.x/;
where ı.x/ D o.x= log x/. A natural question is: how small is the “error term” ı.x/? It can be shown that .x/ D x= log x C O.x=.log x/2 /:
(5.10)
This bound on the error term is not very impressive. The problem is that x= log x is not really the best “simple” function that approximates .x/. It turns out that a better approximation to .x/ is the logarithmic integral, defined for real numbers x 2 by Z x dt li.x/ WD : 2 log t It is not hard to show (see Exercise 5.6) that li.x/ D x= log x C O.x=.log x/2 /:
(5.11)
Thus, li.x/ x= log x .x/. However, the error term in the approximation of .x/ by li.x/ is much better. This is illustrated numerically in Table 5.2; for example, at x D 1018 , li.x/ approximates .x/ with a relative error just under 10 9 , while x= log x approximates .x/ with a relative error of about 0:025. The sharpest proven result on the error in approximating .x/ by li.x/ is the following:
5.5 The prime number theorem . . . and beyond
Theorem 5.15. Let .x/ WD .log x/3=5 .log log x/ have
1=5 .
c.x/
.x/ D li.x/ C O.xe
117
Then for some c > 0, we
/:
Proof. Literature—see §5.6. Note that the error term xe c.x/ is o.x=.log x/k / for every fixed k 0. Also note that (5.10) follows directly from (5.11) and Theorem 5.15. Although the above estimate on the error term in the approximation of .x/ by li.x/ is pretty good, it is conjectured that the actual error term is much smaller: Conjecture 5.16. For all x 2:01, we have j.x/
li.x/j < x 1=2 log x:
Conjecture 5.16 is equivalent to a famous conjecture called the Riemann hypothesis, which is an assumption about the location of the zeros of a certain function, called Riemann’s zeta function. We give a very brief, high-level account of this conjecture, and its connection to the theory of the distribution of primes. For real numbers s > 1, the zeta function is defined as 1 X 1 .s/ WD : ns
(5.12)
nD1
Note that because s > 1, the infinite series defining .s/ converges. A simple, but important, connection between the zeta function and the theory of prime numbers is the following: Theorem 5.17 (Euler’s identity). For every real number s > 1, we have Y .s/ D .1 p s / 1 ;
(5.13)
p
where the product is over all primes p. Proof. The rigorous interpretation of the infinite product on the right-hand side of (5.13) is as a limit of finite products. Thus, if pi denotes the i th prime, for i D 1; 2; : : : ; then we are really proving that .s/ D lim
r!1
r Y
.1
pi s /
1
i D1
Now, from the identity .1
pi s /
1
D
1 X eD0
pi
es
;
:
118
The distribution of primes
we have r Y
.1
pi s /
1
D 1 C p1 s C p1 2s C 1 C pr s C pr 2s C
i D1
D
1 X hr .n/ ; ns
nD1
where hr .n/ WD
1 if n is divisible only by the primes p1 ; : : : ; pr ; 0 otherwise.
Here, we have made use of the fact (see §A7) that we can multiply term-wise infinite series with non-negative terms. P s < (because the Now, for every > 0, there exists n0 such that 1 nDn0 n series defining .s/ converges). Moreover, there exists an r0 such that hr .n/ D 1 for all n < n0 and r r0 . Therefore, for all r r0 , we have ˇX ˇ 1 X ˇ 1 hr .n/ ˇ ˇ ˇ .s/ n s < : ˇ ˇ ns nDn nD1
0
It follows that 1 X hr .n/ D .s/; lim r!1 ns nD1
which proves the theorem. While Theorem 5.17 is nice, things become much more interesting if one extends the domain of definition of the zeta function to the complex plane. For the reader who is familiar with just a little complex analysis, it is easy to see that the infinite series defining the zeta function in (5.12) converges absolutely for all complex numbers s whose real part is greater than 1, and that (5.13) holds as well for such s. However, it is possible to extend the domain of definition of even further — in fact, one can extend the definition of in a “nice way ” (in the language of complex analysis, analytically continue) to the entire complex plane (except the point s D 1, where there is a simple pole). Exactly how this is done is beyond the scope of this text, but assuming this extended definition of , we can now state the Riemann hypothesis: Conjecture 5.18 (Riemann hypothesis). Let s be a complex number with s D x C yi, x; y 2 R. Then .s/ D 0 and 0 < x < 1=2 implies x D 1=2. A lot is known about the zeros of the zeta function in the “critical strip,” which consists of those points s whose real part is greater than 0 and less than 1: it is
5.5 The prime number theorem . . . and beyond
119
known that there are infinitely many such zeros, and there are even good estimates about their density. It turns out that one can apply standard tools in complex analysis, like contour integration, to the zeta function (and functions derived from it) to answer various questions about the distribution of primes. Indeed, such techniques may be used to prove the prime number theorem. However, if one assumes the Riemann hypothesis, then these techniques yield much sharper results, such as the bound in Conjecture 5.16. E XERCISE 5.23. For any arithmetic function a (mapping positive integers to reals), we can form the Dirichlet series 1 X a.n/ Fa .s/ WD : ns nD1
For simplicity we assume that s takes only real values, even though such series are usually studied for complex values of s. (a) Show that if the Dirichlet series Fa .s/ converges absolutely for some real s, then it converges absolutely for all real s 0 s. (b) From part (a), conclude that for any given arithmetic function a, there is an interval of absolute convergence of the form .s0 ; 1/, where we allow s0 D 1 and s0 D 1, such that Fa .s/ converges absolutely for s > s0 , and does not converge absolutely for s < s0 . (c) Let a and b be arithmetic functions such that Fa .s/ has an interval of absolute convergence .s0 ; 1/ and Fb .s/ has an interval of absolute convergence .s00 ; 1/, and assume that s0 < 1 and s00 < 1. Let c WD a ? b be the Dirichlet product of a and b, as defined in §2.9. Show that for all s 2 .max.s0 ; s00 /; 1/, the series Fc .s/ converges absolutely and, moreover, that Fa .s/Fb .s/ D Fc .s/. 5.5.3 Explicit estimates Sometimes, it is useful to have explicit estimates for .x/, as well as related functions, like #.x/ and the nth prime function pn . The following theorem presents a number of bounds that have been proved without relying on any unproved conjectures. Theorem 5.19. We have: x 1 x 3 (i) 1C < .x/ < 1C ; for x 59; log x 2 log x log x 2 log x (ii) n.log n C log log n 3=2/ < pn < n.log n C log log n 1=2/; for n 20;
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The distribution of primes
1 1 < #.x/ < x 1 C ; for x 563; 2 log x 2 log x X 1 1 < 1=p < log log x C A C ; (iv) log log x C A 2 2.log x/ 2.log x/2 px
(iii) x 1
for x 286, where A 0:261497212847643; Y 1 B1 1 B1 1 1 (v) 1 < < 1 C ; log x 2.log x/2 p log x 2.log x/2 px for x 285, where B1 0:561459483566885. Proof. Literature—see §5.6. 5.5.4 Primes in arithmetic progressions In Theorems 2.35 and 2.36, we proved that there are infinitely many primes p 1 .mod 4/ and infinitely many primes p 3 .mod 4/. These results are actually special cases of a much more general result. Let d be a positive integer, and let a be any integer. An arithmetic progression with first term a and common difference d consists of all integers of the form a C d m; m D 0; 1; 2; : : : : The question is: under what conditions does such an arithmetic progression contain infinitely many primes? An equivalent formulation is: under what conditions are there infinitely many primes p a .mod d /? If a and d have a common factor c > 1, then every term in the progression is divisible by c, and so there can be at most one prime in the progression. So a necessary condition for the existence of infinitely many primes p a .mod d / is that gcd.a; d / D 1. A famous theorem due to Dirichlet states that this is a sufficient condition as well. Theorem 5.20 (Dirichlet’s theorem). Let a; d 2 Z with d > 0 and gcd.a; d / D 1. Then there are infinitely many primes p a .mod d /. Proof. Literature—see §5.6. We can also ask about the density of primes in arithmetic progressions. One might expect that for a fixed value of d , the primes are distributed in roughly equal measure among the .d / different residue classes Œad with gcd.a; d / D 1. This is in fact the case. To formulate such assertions, we define .xI d; a/ to be the number of primes p up to x with p a .mod d /. Theorem 5.21. Let a; d 2 Z with d > 0 and gcd.a; d / D 1. Then x .xI d; a/ : .d / log x
5.5 The prime number theorem . . . and beyond
121
Proof. Literature—see §5.6. The above theorem is only applicable in the case where d and a are fixed as x ! 1. For example, it says that roughly half the primes up to x are congruent to 1 modulo 4, and roughly half the primes up to x are congruent to 3 modulo 4. However, suppose d ! 1, and we want to estimate, say, the number of primes p 1 .mod d / up to d 3 . Theorem 5.21 does not help us here. The following conjecture does, however: Conjecture 5.22. Let x 2 R, a; d 2 Z with x 2, d 2, and gcd.a; d / D 1. Then ˇ li.x/ ˇˇ ˇ ˇ.xI d; a/ ˇ x 1=2 .log x C 2 log d /: .d / The above conjecture is in fact a consequence of a generalization of the Riemann hypothesis — see §5.6. This conjecture implies that for every constant ˛ < 1=2, if 2 d x ˛ , then .xI d; a/ is closely approximated by li.x/= .d / (see Exercise 5.24). It can also be used to get an upper bound on the least prime p a .mod d / (see Exercise 5.25). The following theorem is the best rigorously proven upper bound on the smallest prime in an arithmetic progression: Theorem 5.23. There exists a constant c such that for all a; d 2 Z with d 2 and gcd.a; d / D 1, the least prime p a .mod d / is at most cd 11=2 . Proof. Literature—see §5.6. E XERCISE 5.24. Assuming Conjecture 5.22, show that for all ˛; , with 0 < ˛ < 1=2 and 0 < < 1, there exists an x0 , such that for all x > x0 , for all d 2 Z with 2 d x ˛ , and for all a 2 Z relatively prime to d , the number of primes p x such that p a .mod d / is at least .1 / li.x/= .d / and at most .1 C / li.x/= .d /. E XERCISE 5.25. Assuming Conjecture 5.22, show that there exists a constant c such that for all a; d 2 Z with d 2 and gcd.a; d / D 1, the least prime p a .mod d / is at most c .d /2 .log d /4 . 5.5.5 Sophie Germain primes A Sophie Germain prime is a prime p such that 2p C1 is also prime. Such primes are actually useful in a number of practical applications, and so we discuss them briefly here. It is an open problem to prove (or disprove) that there are infinitely many Sophie Germain primes. However, numerical evidence, and heuristic arguments, strongly
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The distribution of primes
suggest not only that there are infinitely many such primes, but also a fairly precise estimate on the density of such primes. Let .x/ denote the number of Sophie Germain primes up to x. Conjecture 5.24. We have .x/ C
x ; .log x/2
where C is the constant C WD 2
Y q.q 2/ 1:32032; .q 1/2
q>2
and the product is over all primes q > 2. The above conjecture is a special case of the following, more general conjecture. Conjecture 5.25 (Dickson’s conjecture). Let .a1 ; b1 /; : : : ; .ak ; bk / be distinct pairs of integers, where each ai is positive. Let P .x/ be the number of positive integers m up to x such that ai m C bi are simultaneously prime for i D 1; : : : ; k. For each prime p, let !.p/ be the number of integers m 2 f0; : : : ; p 1g that satisfy k Y
.ai m C bi / 0 .mod p/:
i D1
If !.p/ < p for each prime p, then P .x/ D
x ; .log x/k
where D WD
Y1 p
!.p/=p ; .1 1=p/k
the product being over all primes p. In Exercise 5.26 below, you are asked to verify that the quantity D appearing in Conjecture 5.25 satisfies 0 < D < 1. Conjecture 5.24 is implied by Conjecture 5.25 with k WD 2, .a1 ; b1 / WD .1; 0/, and .a2 ; b2 / WD .2; 1/; in this case, !.2/ D 1 and !.p/ D 2 for all p > 2. The above conjecture also includes (a strong version of) the famous twin primes conjecture as a special case: the number of primes p up to x such that p C 2 is also prime is C x=.log x/2 , where C is the same constant as in Conjecture 5.24. A heuristic argument in favor of Conjecture 5.25 runs as follows. In some sense, the chance that a large positive integer m is prime is about 1= log m. Since
5.6 Notes
123
log.ai m C bi / log m, the chance that a1 m C b1 ; : : : ; ak m C bk are all prime should be about 1=.log m/k . But this ignores the fact that a1 m C b1 ; : : : ; ak m C bk are not quite random integers. For each prime p, we must apply a “correction factor” rp =sp , where rp is the chance that for random m, none of a1 mCb1 ; : : : ; ak mC bk is divisible by p, and sp is the chance that for k truly random, large integers, none of them is divisible by p. One sees that rp D 1 !.p/=p and sp D .1 1=p/k . This implies (using §A5 and Exercise 5.6) that P .x/ should be about Z x X D 1=.log m/k D dt =.log t/k Dx=.log x/k : mx
2
Although Conjecture 5.25 is well supported by numerical evidence, there seems little hope of it being proved any time soon, even under the Riemann hypothesis or any of its generalizations. E XERCISE 5.26. Show that the quantity D appearing in Conjecture 5.25 satisfies 0 < D < 1. Hint: first show that !.p/ D k for all sufficiently large p. E XERCISE 5.27. Derive Theorem 5.21 from Conjecture 5.25. E XERCISE 5.28. Show that the constant C appearing in Conjecture 5.24 satisfies 2C D B2 =B12 ; where B1 and B2 are the constants from Exercises 5.12 and 5.13. 5.6 Notes The prime number theorem was conjectured by Gauss in 1791. It was proven independently in 1896 by Hadamard and de la Vallée Poussin. A proof of the prime number theorem may be found, for example, in the book by Hardy and Wright [45]. Theorem 5.19, as well as the estimates for the constants A, B1 , and B2 mentioned in that theorem and Exercises 5.11, 5.12, and 5.13, are from Rosser and Schoenfeld [81]. Theorem 5.15 is from Walfisz [100]. Theorem 5.17, which made the first connection between the theory of prime numbers and the zeta function, was discovered in the 18th century by Euler. The Riemann hypothesis was made by Riemann in 1859, and to this day, remains one of the most vexing conjectures in mathematics. Riemann in fact showed that his conjecture about the zeros of the zeta function is equivalent to the conjecture that
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The distribution of primes
for each fixed > 0, .x/ D li.x/ C O.x 1=2C /. This was strengthened by von Koch in 1901, who showed that the Riemann hypothesis is true if and only if .x/ D li.x/ C O.x 1=2 log x/. See Chapter 1 of the book by Crandall and Pomerance [30] for more on the connection between the Riemann hypothesis and the theory of prime numbers; in particular, see Exercise 1.36 in that book for an outline of a proof that Conjecture 5.16 follows from the Riemann hypothesis. A warning: some authors (and software packages) define the logarithmic integral using the interval of integration .0; x/, rather than .2; x/, which increases its value by a constant c 1:0452. Theorem 5.20 was proved by Dirichlet in 1837, while Theorem 5.21 was proved by de la Vallée Poussin in 1896. A result of Oesterlé [71] implies that Conjecture 5.22 for d 3 is a consequence of an assumption about the location of the zeros of certain generalizations of Riemann’s zeta function; the case d D 2 follows from the bound in Conjecture 5.16 under the ordinary Riemann hypothesis. Theorem 5.23 is from Heath-Brown [46]. The bound in Exercise 5.25 can be improved to c .d /2 .log d /2 (see Theorem 8.5.8 of [11]). Conjecture 5.25 originates from Dickson [33]. In fact, Dickson only conjectured that the quantity P .x/ defined in Conjecture 5.25 tends to infinity. The conjectured formula for the rate of growth of P .x/ is a special case of a more general conjecture stated by Bateman and Horn [12], which generalizes various, more specific conjectures stated by Hardy and Littlewood [44]. For the reader who is interested in learning more on the topics discussed in this chapter, we recommend the books by Apostol [8] and Hardy and Wright [45]; indeed, many of the proofs presented in this chapter are minor variations on proofs from these two books. Our proof of Bertrand’s postulate is based on the presentation in Section 9.2 of Redmond [78]. See also Bach and Shallit [11] (especially Chapter 8), as well as Crandall and Pomerance [30] (especially Chapter 1), for a more detailed overview of these topics. The data in Tables 5.1 and 5.2 was obtained using the computer program Maple.
6 Abelian groups
This chapter introduces the notion of an abelian group. This is an abstraction that models many different algebraic structures, and yet despite the level of generality, a number of very useful results can be easily obtained. 6.1 Definitions, basic properties, and examples Definition 6.1. An abelian group is a set G together with a binary operation ? on G such that (i) for all a; b; c 2 G, a ? .b ? c/ D .a ? b/ ? c (i.e., ? is associative), (ii) there exists e 2 G (called the identity element) such that for all a 2 G, a ? e D a D e ? a, (iii) for all a 2 G there exists a0 2 G (called the inverse of a) such that a ?a0 D e D a0 ? a, (iv) for all a; b 2 G, a ? b D b ? a (i.e., ? is commutative). While there is a more general notion of a group, which may be defined simply by dropping property (iv) in Definition 6.1, we shall not need this notion in this text. The restriction to abelian groups helps to simplify the discussion significantly. Because we will only be dealing with abelian groups, we may occasionally simply say “group” instead of “abelian group.” Before looking at examples, let us state some very basic properties of abelian groups that follow directly from the definition: Theorem 6.2. Let G be an abelian group with binary operation ?. Then we have: (i) G contains only one identity element; (ii) every element of G has only one inverse.
125
126
Abelian groups
Proof. Suppose e; e 0 are both identities. Then we have e D e ? e0 D e0; where we have used part (ii) of Definition 6.1, once with e 0 as the identity, and once with e as the identity. That proves part (i) of the theorem. To prove part (ii) of the theorem, let a 2 G, and suppose that a has two inverses, 0 a and a00 . Then using parts (i)–(iii) of Definition 6.1, we have a0 D a0 ? e (by part (ii)) D a0 ? .a ? a00 / (by part (iii) with inverse a00 of a) D .a0 ? a/ ? a00 (by part (i)) D e ? a00 (by part (iii) with inverse a0 of a) D a00 (by part (ii)). These uniqueness properties justify use of the definite article in Definition 6.1 in conjunction with the terms “identity element” and “inverse.” Note that we never used part (iv) of the definition in the proof of the above theorem. Abelian groups are lurking everywhere, as the following examples illustrate. Example 6.1. The set of integers Z under addition forms an abelian group, with 0 being the identity, and a being the inverse of a 2 Z. Example 6.2. For each integer n, the set nZ D fnz W z 2 Zg under addition forms an abelian group, again, with 0 being the identity, and n. z/ being the inverse of nz. Example 6.3. The set of non-negative integers under addition does not form an abelian group, since additive inverses do not exist for positive integers. Example 6.4. The set of integers under multiplication does not form an abelian group, since inverses do not exist for integers other than ˙1. Example 6.5. The set of integers f˙1g under multiplication forms an abelian group, with 1 being the identity, and 1 its own inverse. Example 6.6. The set of rational numbers Q D fa=b W a; b 2 Z; b ¤ 0g under addition forms an abelian group, with 0 being the identity, and . a/=b being the inverse of a=b. Example 6.7. The set of non-zero rational numbers Q under multiplication forms an abelian group, with 1 being the identity, and b=a being the inverse of a=b. Example 6.8. The set Zn under addition forms an abelian group, where Œ0n is the identity, and where Œ an is the inverse of Œan .
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127
Example 6.9. The set Zn of residue classes Œan with gcd.a; n/ D 1 under multiplication forms an abelian group, where Œ1n is the identity, and if b is a multiplicative inverse of a modulo n, then Œbn is the inverse of Œan . Example 6.10. For every positive integer n, the set of n-bit strings under the “exclusive or” operation forms an abelian group, where the “all zero” bit string is the identity, and every bit string is its own inverse. Example 6.11. The set F of all arithmetic functions f , such that f .1/ ¤ 0, and with the Dirichlet product as the binary operation (see §2.9) forms an abelian group. The special function ı is the identity, and inverses are guaranteed by Exercise 2.54. Example 6.12. The set of all finite bit strings under concatenation does not form an abelian group. Although concatenation is associative and the empty string acts as an identity element, inverses do not exist (except for the empty string), nor is concatenation commutative. Example 6.13. The set of 2 2 integer matrices with determinant ˙1, together with the binary operation of matrix multiplication, is an example of a non-abelian group; that is, it satisfies properties (i)–(iii) of Definition 6.1, but not property (iv). Example 6.14. The set of all permutations on a given set of size n 3, together with the binary operation of function composition, is another example of a nonabelian group (for n D 1; 2, it is an abelian group). Consider an abelian group G with binary operation ?. Since the group operation is associative, for all a1 ; : : : ; ak 2 G, we may write a1 ? ? ak without parentheses, and there can be no ambiguity as to the value of such an expression: any explicit parenthesization of this expression yields the same value. Furthermore, since the group operation is commutative, reordering the ai ’s does not change this value. Note that in specifying a group, one must specify both the underlying set G as well as the binary operation; however, in practice, the binary operation is often implicit from context, and by abuse of notation, one often refers to G itself as the group. For example, when talking about the abelian groups Z and Zn , it is understood that the group operation is addition, while when talking about the abelian group Zn , it is understood that the group operation is multiplication. Typically, instead of using a special symbol like “?” for the group operation, one uses the usual addition (“C”) or multiplication (“”) operations. Additive notation. If an abelian group G is written additively, using “C” as the
128
Abelian groups
group operation, then the identity element is denoted by 0G , and the inverse of an element a 2 G is denoted by a. For a; b 2 G, a b denotes a C . b/. Multiplicative notation. If an abelian group G is written multiplicatively, using “” as the group operation, then the identity element is denoted by 1G , and the inverse of an element a 2 G is denoted by a 1 . As usual, one may write ab in place of a b. Also, one may write a=b for ab 1 . For any particular, concrete abelian group, the most natural choice of notation is clear (e.g., addition for Z and Zn , multiplication for Zn ); however, for a “generic” group, the choice is largely a matter of taste. By convention, whenever we consider a “generic” abelian group, we shall use additive notation for the group operation, unless otherwise specified. The next theorem states a few simple but useful properties of abelian groups (stated using our default, additive notation). Theorem 6.3. Let G be an abelian group. Then for all a; b; c 2 G, we have: (i) if a C b D a C c, then b D c; (ii) the equation a C x D b has a unique solution x 2 G; (iii)
.a C b/ D . a/ C . b/;
(iv)
. a/ D a.
Proof. These statements all follow easily from Definition 6.1 and Theorem 6.2. For (i), just add a to both sides of the equation a C b D a C c. For (ii), the solution is x D b a. For (iii), we have .a C b/ C .. a/ C . b// D .a C . a// C .b C . b// D 0G C 0G D 0G ; which shows that . a/ C . b/ is indeed the inverse of a C b. For (iv), we have . a/ C a D 0G , which means that a is the inverse of a. Part (i) of the above theorem is the cancellation law for abelian groups. P If a1 ; : : : ; ak are elements of an abelian group G, we naturally write kiD1 ai for their sum. By convention, the sum is 0G when k D 0. Part (iii) of Theorem 6.3 Pk Pk obviously generalizes, so that i D1 ai D i D1 . ai /. In the special case where P all the ai ’s have the same value a, we define k a WD kiD1 a. In particular, the inverse of k a is k . a/, which we may write as . k/a. Thus, the notation k a is defined for all integers k. Observe that by definition, 1 a D a and . 1/ a D a. Theorem 6.4. Let G be an abelian group. Then for all a; b 2 G and k; ` 2 Z, we have: (i) k.`a/ D .k`/a D `.ka/; (ii) .k C `/a D ka C `a;
6.1 Definitions, basic properties, and examples
129
(iii) k.a C b/ D ka C kb. Proof. The proof of this is easy, but tedious. We leave the details as an exercise to the reader. Multiplicative notation: It is perhaps helpful to translate the above discussion from additive to multiplicative notation. If a group G is written using multiplicative notation, then Theorem 6.3 says that (i) ab D ac implies b D c, (ii) ax D b has a unique solution, (iii) .ab/ 1 D a 1 b 1 , and (iv) .a 1 / 1 D a. Q If a1 ; : : : ; ak 2 G, we write their product as kiD1 ai , which is 1G when k D 0. Q Q Q We have . kiD1 ai / 1 D kiD1 ai 1 . We define ak WD kiD1 a, and we have .ak / 1 D .a 1 /k , which we may write as a k . Theorem 6.4 says that (i) .a` /k D ak` D .ak /` , (ii) akC` D ak a` , and (iii) .ab/k D ak b k . An abelian group G may be infinite or finite. If the group is finite, we define its order to be the number of elements in the underlying set G; otherwise, we say that the group has infinite order. Example 6.15. The order of the additive group Zn is n. Example 6.16. The order of the multiplicative group Zn is .n/, where is Euler’s phi function, defined in §2.6. Example 6.17. The additive group Z has infinite order. We close this section with two simple constructions for combining groups to build new groups. Example 6.18. If G1 ; : : : ; Gk are abelian groups, we can form the direct product H WD G1 Gk , which consists of all k-tuples .a1 ; : : : ; ak / with a1 2 G1 ; : : : ; ak 2 Gk . We can view H in a natural way as an abelian group if we define the group operation component-wise: .a1 ; : : : ; ak / C .b1 ; : : : ; bk / WD .a1 C b1 ; : : : ; ak C bk /: Of course, the groups G1 ; : : : ; Gk may be different, and the group operation applied in the i th component corresponds to the group operation associated with Gi . We leave it to the reader to verify that H is in fact an abelian group, where 0H D .0G1 ; : : : ; 0Gk / and .a1 ; : : : ; ak / D . a1 ; : : : ; ak /. As a special case, if G D G1 D D Gk , then the k-wise direct product of G is denoted G k . Example 6.19. Let G be an abelian group. An element .a1 ; : : : ; ak / of G k may be identified with the function f W f1; : : : ; kg ! G given by f .i / D ai for i D 1; : : : ; k. We can generalize this, replacing f1; : : : ; kg by an arbitrary set I . We define Map.I; G/ to be the set of all functions f W I ! G, which we naturally
130
Abelian groups
view as a group by defining the group operation point-wise: for f; g 2 Map.I; G/, we define .f C g/.i / WD f .i / C g.i / for all i 2 I : Again, we leave it to the reader to verify that Map.I; G/ is an abelian group, where the identity element is the function that maps each i 2 I to 0G , and for f 2 Map.I; G/, we have . f /.i / D .f .i // for all i 2 I . E XERCISE 6.1. For a finite abelian group, one can completely specify the group by writing down the group operation table. For instance, Example 2.6 presented an addition table for Z6 . (a) Write down group operation tables for the following finite abelian groups: Z5 , Z5 , and Z3 Z4 . (b) Show that the group operation table for every finite abelian group is a Latin square; that is, each element of the group appears exactly once in each row and column. (c) Below is an addition table for an abelian group that consists of the elements fa; b; c; d g; however, some entries are missing. Fill in the missing entries. C a b c d
a a b
b
c
d
a a
E XERCISE 6.2. For a; b 2 R, define a ? b WD ab x > 1g. Show that:
a
b C 2. Let G WD fx 2 R W
(a) G is closed under ?; (b) the set G under the operation ? forms an abelian group. E XERCISE 6.3. Let G be an abelian group, and let g be an arbitrary, fixed element of G. Assume that the group operation of G is written additively. We define a new binary operation ˇ on G, as follows: for a; b 2 G, let a ˇ b WD a C b C g. Show that the set G under ˇ forms an abelian group. E XERCISE 6.4. Let G be a finite abelian group of even order. Show that there exists a 2 G with a ¤ 0G and 2a D 0G . E XERCISE 6.5. Let ? be a binary operation on a finite set G. Assume that ? is associative, commutative, and satisfies the cancellation law: a ? b D a ? c implies b D c. Show that G under ? forms an abelian group.
6.2 Subgroups
131
E XERCISE 6.6. Show that the result of the previous exercise need not hold if G is infinite. 6.2 Subgroups We next introduce the notion of a subgroup. Definition 6.5. Let G be an abelian group, and let H be a non-empty subset of G such that (i) a C b 2 H for all a; b 2 H , and (ii) a 2 H for all a 2 H . Then H is called a subgroup of G. In words: H is a subgroup of G if it is closed under the group operation and taking inverses. Multiplicative notation: if the abelian group G in the above definition is written using multiplicative notation, then H is a subgroup if ab 2 H and a 1 2 H for all a; b 2 H . Theorem 6.6. If G is an abelian group, and H is a subgroup of G, then H contains 0G ; moreover, the binary operation of G, when restricted to H , yields a binary operation that makes H into an abelian group whose identity is 0G . Proof. First, to see that 0G 2 H , just pick any a 2 H , and using both properties of the definition of a subgroup, we see that 0G D a C . a/ 2 H . Next, note that by property (i) of Definition 6.5, H is closed under addition, which means that the restriction of the binary operation “C” on G to H induces a well defined binary operation on H . So now it suffices to show that H , together with this operation, satisfies the defining properties of an abelian group. Associativity and commutativity follow directly from the corresponding properties for G. Since 0G acts as the identity on G, it does so on H as well. Finally, property (ii) of Definition 6.5 guarantees that every element a 2 H has an inverse in H , namely, a. Clearly, for an abelian group G, the subsets G and f0G g are subgroups. These are not very interesting subgroups. Other, more interesting subgroups may sometimes be found by using the following two theorems. Theorem 6.7. Let G be an abelian group, and let m be an integer. Then mG WD fma W a 2 Gg is a subgroup of G. Proof. mG is non-empty, since 0G D m0G 2 mG. For ma; mb 2 mG, we have ma C mb D m.a C b/ 2 mG, and .ma/ D m. a/ 2 mG.
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Abelian groups
Theorem 6.8. Let G be an abelian group, and let m be an integer. Then Gfmg WD fa 2 G W ma D 0G g is a subgroup of G. Proof. Gfmg is non-empty, since m0G D 0G , and so Gfmg contains 0G . If ma D 0G and mb D 0G , then m.a C b/ D ma C mb D 0G C 0G D 0G and m. a/ D .ma/ D 0G D 0G . Multiplicative notation: if the abelian group G in the above two theorems is written using multiplicative notation, then we write the subgroup of the first theorem as G m WD fam W a 2 Gg. The subgroup in the second theorem is denoted in the same way: Gfmg WD fa 2 G W am D 1G g. Example 6.20. We already proved that .Zn /m is a subgroup of Zn in Theorem 2.16. Also, the proof of Theorem 2.17 clearly works for an arbitrary abelian group G: for each a 2 G, and all `; m 2 Z with gcd.`; m/ D 1, if `a 2 mG, then a 2 mG. Example 6.21. Let p be an odd prime. Then as we saw in Theorem 2.20, .Zp /2 is a subgroup of Zp of order .p 1/=2, and as we saw in Theorem 2.18, Zp f2g D fŒ˙1g. Example 6.22. For every integer m, the set mZ is the subgroup of the additive group Z consisting of all multiples of m. This is the same as the ideal of Z generated by m, which we already studied in some detail in §1.2. Two such subgroups mZ and m0 Z are equal if and only if m D ˙m0 . The subgroup Zfmg is equal to Z if m D 0, and is equal to f0g otherwise. Example 6.23. Let n be a positive integer, let m 2 Z, and consider the subgroup mZn of the additive group Zn . Now, for every residue class Œz 2 Zn , we have mŒz D Œmz. Therefore, Œb 2 mZn if and only if there exists z 2 Z such that mz b .mod n/. By Theorem 2.5, such a z exists if and only if d j b, where d WD gcd.m; n/. Thus, mZn consists precisely of the n=d distinct residue classes Œi d .i D 0; : : : ; n=d
1/;
and in particular, mZn D d Zn . Now consider the subgroup Zn fmg of Zn . The residue class Œz is in Zn fmg if and only if mz 0 .mod n/. By Theorem 2.5, this happens if and only if z 0 .mod n=d /, where d WD gcd.m; n/ as above. Thus, Zn fmg consists precisely of the d residue classes Œi n=d .i D 0; : : : ; d and in particular, Zn fmg D Zn fd g D .n=d /Zn .
1/;
6.2 Subgroups
133
Example 6.24. For n D 15, consider again the table in Example 2.2. For m D 1; 2; 3; 4; 5; 6, the elements appearing in the mth row of that table form the subgroup mZn of Zn , and also the subgroup Zn fn=d g, where d WD gcd.m; n/. Because the abelian groups Z and Zn are of such importance, it is a good idea to completely characterize all subgroups of these abelian groups. As the following two theorems show, the subgroups in Examples 6.22 and 6.23 are the only subgroups of these groups. Theorem 6.9. If G is a subgroup of Z, then there exists a unique non-negative integer m such that G D mZ. Moreover, for two non-negative integers m1 and m2 , we have m1 Z m2 Z if and only if m2 j m1 . Proof. Actually, we have already proven this. One only needs to observe that a subset G of Z is a subgroup if and only if it is an ideal of Z, as defined in §1.2 (see Exercise 1.8). The first statement of the theorem then follows from Theorem 1.6. The second statement follows easily from the definitions, as was observed in §1.2. Theorem 6.10. If G is a subgroup of Zn , then there exists a unique positive integer d dividing n such that G D d Zn . Also, for positive divisors d1 ; d2 of n, we have d1 Zn d2 Zn if and only if d2 j d1 . Proof. Note that the second statement implies the uniqueness part of the first statement, so it suffices to prove just the existence part of the first statement and the second statement. Let G be an arbitrary subgroup of Zn , and let H WD fz 2 Z W Œz 2 Gg. We claim that H is a subgroup of Z. To see this, observe that if a; b 2 H , then Œa and Œb belong to G, and hence so do Œa C b D Œa C Œb and Œ a D Œa, and thus a C b and a belong to H . That proves the claim, and Theorem 6.9 implies that H D d Z for some non-negative integer d . It follows that G D fŒy W y 2 H g D fŒdz W z 2 Zg D d Zn : Evidently, n 2 H D d Z, and hence d j n. That proves the existence part of the first statement of the theorem. To prove the second statement of the theorem, observe that if d1 and d2 are arbitrary integers, then d1 Zn d2 Zn ” d2 z d1 .mod n/ for some z 2 Z ” gcd.d2 ; n/ j d1 (by Theorem 2.5): In particular, if d2 j n, then gcd.d2 ; n/ D d2 , which proves the second statement.
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Abelian groups
Of course, not all abelian groups have such a simple subgroup structure. Example 6.25. Consider the group G D Z2 Z2 . For every non-zero ˛ 2 G, ˛ C ˛ D 0G . From this, it is clear that the set H D f0G ; ˛g is a subgroup of G. However, for every integer m, mG D G if m is odd, and mG D f0G g if m is even. Thus, the subgroup H is not of the form mG for any m. Example 6.26. Consider the group Z15 . We can enumerate its elements as Z15 D fŒ˙1; Œ˙2; Œ˙4; Œ˙7g. Therefore, the elements of .Z15 /2 are Œ12 D Œ1; Œ22 D Œ4; Œ42 D Œ16 D Œ1; Œ72 D Œ49 D Œ4I thus, .Z15 /2 has order 2, consisting as it does of the two distinct elements Œ1 and Œ4. Going further, one sees that .Z15 /4 D fŒ1g. Thus, ˛ 4 D Œ1 for all ˛ 2 Z15 . By direct calculation, one can determine that .Z15 /3 D Z15 ; that is, cubing simply permutes Z15 . For any given integer m, write m D 4q C r, where 0 r < 4. Then for every ˛ 2 Z15 , we have ˛ m D ˛ 4qCr D ˛ 4q ˛ r D ˛ r . Thus, .Z15 /m is either Z15 , .Z15 /2 , or fŒ1g. However, there are certainly other subgroups of Z15 — for example, the subgroup fŒ˙1g. Example 6.27. Consider the group Z5 D fŒ˙1; Œ˙2g. The elements of .Z5 /2 are Œ12 D Œ1; Œ22 D Œ4 D Œ 1I thus, .Z5 /2 D fŒ˙1g and has order 2. There are in fact no other subgroups of Z5 besides Z5 , fŒ˙1g, and fŒ1g. Indeed, if H is a subgroup containing Œ2, then we must have H D Z5 : Œ2 2 H implies Œ22 D Œ4 D Œ 1 2 H , which implies Œ 2 2 H as well. The same holds if H is a subgroup containing Œ 2. Example 6.28. Consider again the abelian group F of arithmetic functions f , such that f .1/ ¤ 0, and with the Dirichlet product as the binary operation, as discussed in Example 6.11. Exercises 2.48 and 2.55 imply that the subset of all multiplicative functions is a subgroup. We close this section with two theorems that provide useful ways to build new subgroups out of old subgroups. Theorem 6.11. If H1 and H2 are subgroups of an abelian group G, then so is H1 C H2 WD fa1 C a2 W a1 2 H1 ; a2 2 H2 g:
135
6.2 Subgroups
Proof. It is evident that H1 C H2 is non-empty, as it contains 0G C 0G D 0G . Consider two elements in H1 C H2 , which we can write as a1 C a2 and b1 C b2 , where a1 ; b1 2 H1 and a2 ; b2 2 H2 . Then by the closure properties of subgroups, a1 C b1 2 H1 and a2 C b2 2 H2 , and hence .a1 C a2 / C .b1 C b2 / D .a1 C b1 / C .a2 C b2 / 2 H1 C H2 . Similarly, .a1 C a2 / D . a1 / C . a2 / 2 H1 C H2 . Multiplicative notation: if the abelian group G in the above theorem is written multiplicatively, then the subgroup defined in the theorem is written H1 H2 WD fa1 a2 W a1 2 H1 ; a2 2 H2 g. Theorem 6.12. If H1 and H2 are subgroups of an abelian group G, then so is H1 \ H2 . Proof. It is evident that H1 \ H2 is non-empty, as both H1 and H2 contain 0G , and hence so does their intersection. If a 2 H1 \ H2 and b 2 H1 \ H2 , then since a; b 2 H1 , we have a C b 2 H1 , and since a; b 2 H2 , we have a C b 2 H2 ; therefore, a C b 2 H1 \ H2 . Similarly, a 2 H1 and a 2 H2 , and therefore, a 2 H1 \ H2 . Let G be an abelian group and H1 ; H2 ; H3 subgroups of G. The reader may verify that H1 C H2 D H2 C H1 and .H1 C H2 / C H3 D H1 C .H2 C H3 /. It follows that if H1 ; : : : ; Hk are subgroups of G, then we can write H1 C C Hk without any parentheses, and there can be no ambiguity; moreover, the order of the Hi ’s does not matter. The same holds with “C” replaced by “\.” A warning: If H is a subgroup of an abelian group G, then in general, we have H C H ¤ 2H . For example, Z C Z D Z, while 2Z ¤ Z. E XERCISE 6.7. Let G be an abelian group. (a) Show that if H is a subgroup of G and a; b 2 H , then a (b) Suppose that H is a non-empty subset of G such that a a; b 2 H . Show that H is a subgroup of G.
b 2 H. b 2 H for all
E XERCISE 6.8. Let G be an abelian group. (a) Show that if H is a subgroup of G, h 2 H , and g 2 G n H , then h C g 2 G n H. (b) Suppose that H is a non-empty subset of G such that for all h; g 2 G: (i) h 2 H implies h 2 H , and (ii) h 2 H and g 2 G n H implies h C g 2 G n H . Show that H is a subgroup of G. E XERCISE 6.9. Show that if H is a subgroup of an abelian group G, then a set K H is a subgroup of G if and only if K is a subgroup of H .
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Abelian groups
E XERCISE 6.10. Let G be an abelian group with subgroups H1 and H2 . Show that every subgroup H of G that contains H1 [ H2 contains H1 C H2 , and H1 H2 if and only if H1 C H2 D H2 . E XERCISE 6.11. Let H1 be a subgroup of an abelian group G1 and H2 a subgroup of an abelian group G2 . Show that H1 H2 is a subgroup of G1 G2 . E XERCISE 6.12. Show that if G1 and G2 are abelian groups, and m is an integer, then m.G1 G2 / D mG1 mG2 . E XERCISE 6.13. Let G1 and G2 be abelian groups, and let H be a subgroup of G1 G2 . Define H1 WD fa1 2 G1 W .a1 ; a2 / 2 H for some a2 2 G2 g: Show that H1 is a subgroup of G1 . E XERCISE 6.14. Let I be a set and G be an abelian group, and consider the group Map.I; G/ of functions f W I ! G. Let Map# .I; G/ be the set of functions f 2 Map.I; G/ such that f .i / ¤ 0G for at most finitely many i 2 I . Show that Map# .I; G/ is a subgroup of Map.I; G/. 6.3 Cosets and quotient groups We now generalize the notion of a congruence relation. Let G be an abelian group, and let H be a subgroup of G. For a; b 2 G, we write a b .mod H / if a b 2 H . In other words, a b .mod H / if and only if a D b C h for some h 2 H . Analogously to Theorem 2.2, if we view the subgroup H as fixed, then the following theorem says that the binary relation “ .mod H /” is an equivalence relation on the set G: Theorem 6.13. Let G be an abelian group and H a subgroup of G. For all a; b; c 2 G, we have: (i) a a .mod H /; (ii) a b .mod H / implies b a .mod H /; (iii) a b .mod H / and b c .mod H / implies a c .mod H /. Proof. For (i), observe that H contains 0G D a a. For (ii), observe that if H contains a b, then it also contains .a b/ D b a. For (iii), observe that if H contains a b and b c, then it also contains .a b/ C .b c/ D a c. Since the binary relation “ .mod H /” is an equivalence relation, it partitions G into equivalence classes (see Theorem 2.1). For a 2 G, we denote the
6.3 Cosets and quotient groups
137
equivalence class containing a by ŒaH . By definition, we have x 2 ŒaH ” x a .mod H / ” x D a C h for some h 2 H ; and hence ŒaH D a C H WD fa C h W h 2 H g: It is also clear that Œ0G H D H . Historically, these equivalence classes are called cosets of H in G, and we shall adopt this terminology here as well. Any member of a coset is called a representative of the coset. Multiplicative notation: if G is written multiplicatively, then a b .mod H / means ab 1 2 H , and ŒaH D aH WD fah W h 2 H g. Example 6.29. Let G WD Z and H WD nZ for some positive integer n. Then a b .mod H / if and only if a b .mod n/. The coset ŒaH is exactly the same thing as the residue class Œan 2 Zn . Example 6.30. Let G WD Z6 , which consists of the residue classes Œ0; Œ1; Œ2; Œ3; Œ4; Œ5. Let H be the subgroup 3G D fŒ0; Œ3g of G. The coset of H containing the residue class Œ1 is Œ1 C H D fŒ1; Œ4g, and the coset of H containing the residue class Œ2 is Œ2 C H D fŒ2; Œ5g. The cosets fŒ0; Œ3g, fŒ1; Œ4g, and fŒ2; Œ5g are the only cosets of H in G, and they clearly partition the set Z6 . Note that each coset of H in G contains two elements, each of which is itself a coset of 6Z in Z (i.e., a residue classes modulo 6). In the previous example, we saw that each coset contained the same number of elements. As the next theorem shows, this was no accident. Theorem 6.14. Let G be an abelian group, and H a subgroup of G. For all a; b 2 G, the function f W G!G x 7! b
aCx
is a bijection, which when restricted to the coset ŒaH , yields a bijection from ŒaH to the coset ŒbH . In particular, every two cosets of H in G have the same cardinality. Proof. First, we claim that f is a bijection. Indeed, if f .x/ D f .x 0 /, then b a C x D b a C x 0 , and subtracting b and adding a to both sides of this equation yields x D x 0 . That proves that f is injective. To prove that f is surjective, observe that for any given x 0 2 G, we have f .a b C x 0 / D x 0 . Second, we claim that for all x 2 G, we have x 2 ŒaH if and only if f .x/ 2
138
Abelian groups
ŒbH . On the one hand, suppose that x 2 ŒaH , which means that x D a C h for some h 2 H . Subtracting a and adding b to both sides of this equation yields b a C x D b C h, which means f .x/ 2 ŒbH . Conversely, suppose that f .x/ 2 ŒbH , which means that b a C x D b C h for some h 2 H . Subtracting b and adding a to both sides of this equation yields x D a C h, which means that x 2 ŒaH . The theorem is now immediate from these two claims. An incredibly useful consequence of the above theorem is: Theorem 6.15 (Lagrange’s theorem). If G is a finite abelian group, and H is a subgroup of G, then the order of H divides the order of G. Proof. This is an immediate consequence of the previous theorem, and the fact that the cosets of H in G partition G. Analogous to Theorem 2.3, we have: Theorem 6.16. Let G be an abelian group and H a subgroup. For all a; a0 ; b; b 0 2 G, if a a0 .mod H / and b b 0 .mod H /, then a C b a0 C b 0 .mod H /. Proof. Now, a a0 .mod H / and b b 0 .mod H / means that a D a0 C x and b D b 0 C y for some x; y 2 H . Therefore, a C b D .a0 C x/ C .b 0 C y/ D .a0 Cb 0 /C.x Cy/, and since x Cy 2 H , this means that aCb a0 Cb 0 .mod H /. Let G be an abelian group and H a subgroup. Let G=H denote the set of all cosets of H in G. Theorem 6.16 allows us to define a binary operation on G=H in the following natural way: for a; b 2 G, define ŒaH C ŒbH WD Œa C bH : The fact that this definition is unambiguous follows immediately from Theorem 6.16: if ŒaH D Œa0 H and ŒbH D Œb 0 H , then Œa C bH D Œa0 C b 0 H . We can easily verify that this operation makes G=H into an abelian group. We need to check that the four properties of Definition 6.1 are satisfied: (i) Associativity: ŒaH C .ŒbH C ŒcH / D ŒaH C Œb C cH D Œa C .b C c/H D Œ.a C b/ C cH D Œa C bH C ŒcH D .ŒaH C ŒbH / C ŒcH : Here, we have used the definition of addition of cosets, and the corresponding associativity property for G.
6.3 Cosets and quotient groups
139
(ii) Identity element: the coset Œ0G H D H acts as the identity element, since ŒaH C Œ0G H D Œa C 0G H D ŒaH D Œ0G C aH D Œ0G H C ŒaH : (iii) Inverses: the inverse of the coset ŒaH is Œ aH , since ŒaH C Œ aH D Œa C . a/H D Œ0G H D Œ. a/ C aH D Œ aH C ŒaH : (iv) Commutativity: ŒaH C ŒbH D Œa C bH D Œb C aH D ŒbH C ŒaH : The group G=H is called the quotient group of G modulo H . The order of the group G=H is sometimes denoted ŒG W H and is called the index of H in G. Note that if H D G, then the quotient group G=H consists of just a single element, and so ŒG W H D 1. Multiplicative notation: if G is written multiplicatively, then the definition of the group operation of G=H is expressed ŒaH ŒbH WD Œa bH ; the identity element of G=H is Œ1G H D H , and the inverse of ŒaH is Œa 1 H . Theorem 6.17. Let G be a finite abelian group and H a subgroup. Then ŒG W H D jGj=jH j. Moreover, if K is a subgroup of H , then ŒG W K D ŒG W H ŒH W K: Proof. The fact that ŒG W H D jGj=jH j follows directly from Theorem 6.14. The fact that ŒG W K D ŒG W H ŒH W K follows from a simple calculation: ŒG W H D
jGj jGj=jKj ŒG W K D D : jH j jH j=jKj ŒH W K
Example 6.31. For each n 1, the group Zn is precisely the quotient group Z=nZ. Example 6.32. Continuing with Example 6.30, let G WD Z6 and H WD 3G D fŒ0; Œ3g. The quotient group G=H has order 3, and consists of the cosets ˛ WD fŒ0; Œ3g; ˇ WD fŒ1; Œ4g; WD fŒ2; Œ5g: If we write out an addition table for G, grouping together elements in cosets of H in G, then we also get an addition table for the quotient group G=H :
140
Abelian groups
C Œ0 Œ3 Œ1 Œ4 Œ2 Œ5
Œ0 Œ0 Œ3 Œ1 Œ4 Œ2 Œ5
Œ3 Œ3 Œ0 Œ4 Œ1 Œ5 Œ2
Œ1 Œ1 Œ4 Œ2 Œ5 Œ3 Œ0
Œ4 Œ4 Œ1 Œ5 Œ2 Œ0 Œ3
Œ2 Œ2 Œ5 Œ3 Œ0 Œ4 Œ1
Œ5 Œ5 Œ2 Œ0 Œ3 Œ1 Œ4
This table illustrates quite graphically the point of Theorem 6.16: for every two cosets, if we take any element from the first and add it to any element of the second, we always end up in the same coset. We can also write down just the addition table for G=H : C ˛ ˇ
˛ ˛ ˇ
ˇ ˇ
˛
˛ ˇ
Note that by replacing ˛ with Œ03 , ˇ with Œ13 , and with Œ23 , the addition table for G=H becomes the addition table for Z3 . In this sense, we can view G=H as essentially just a “renaming” of Z3 . Example 6.33. Let us return to Example 6.26. The multiplicative group Z15 , as we saw, is of order 8. The subgroup .Z15 /2 of Z15 has order 2. Therefore, the quotient group Z15 =.Z15 /2 has order 4. Indeed, the cosets are ˛00 WD .Z15 /2 D fŒ1; Œ4g;
˛01 WD Œ 1.Z15 /2 D fŒ 1; Œ 4g;
˛10 WD Œ2.Z15 /2 D fŒ2; Œ 7g;
˛11 WD Œ 2.Z15 /2 D fŒ 2; Œ7g:
We can write down the multiplication table for the quotient group: ˛00 ˛01 ˛10 ˛11
˛00 ˛00 ˛01 ˛10 ˛11
˛01 ˛01 ˛00 ˛11 ˛10
˛10 ˛10 ˛11 ˛00 ˛01
˛11 ˛11 ˛10 ˛01 ˛00
Note that this group is essentially just a “renaming” of the additive group Z2 Z2 . Example 6.34. As we saw in Example 6.27, .Z5 /2 D fŒ˙1g. Therefore, the quotient group Z5 =.Z5 /2 has order 2. The cosets of .Z5 /2 in Z5 are ˛0 WD fŒ˙1g and ˛1 WD fŒ˙2g, and the multiplication table looks like this:
6.4 Group homomorphisms and isomorphisms
˛0 ˛1
˛0 ˛0 ˛1
141
˛1 ˛1 ˛0
We see that the quotient group is essentially just a “renaming” of Z2 . E XERCISE 6.15. Write down the cosets of .Z35 /2 in Z35 , along with the multiplication table for the quotient group Z35 =.Z35 /2 . E XERCISE 6.16. Let n be an odd, positive integer whose factorization into primes is n D p1e1 prer . Show that ŒZn W .Zn /2 D 2r . E XERCISE 6.17. Let n be a positive integer, and let m be any integer. Show that ŒZn W mZn D n= gcd.m; n/. E XERCISE 6.18. Let G be an abelian group and H a subgroup with ŒG W H D 2. Show that if a; b 2 G n H , then a C b 2 H . E XERCISE 6.19. Let H be a subgroup of an abelian group G, and let a; b 2 G with a b .mod H /. Show that ka kb .mod H / for all k 2 Z. E XERCISE 6.20. Let G be an abelian group, and let be an equivalence relation on G. Further, suppose that for all a; a0 ; b 2 G, if a a0 , then a C b a0 C b. Let H WD fa 2 G W a 0G g. Show that H is a subgroup of G, and that for all a; b 2 G, we have a b if and only if a b .mod H /. E XERCISE 6.21. Let H be a subgroup of an abelian group G, and let a; b 2 G. Show that Œa C bH D fx C y W x 2 ŒaH ; y 2 ŒbH g. 6.4 Group homomorphisms and isomorphisms In this section, we study maps that relate the structure of one group to another. Such maps are often very useful, as they may allow us to transfer hard-won knowledge about one group to another, perhaps more mysterious, group. Definition 6.18. A group homomorphism is a function from an abelian group G to an abelian group G 0 such that .a C b/ D .a/ C .b/ for all a; b 2 G. Note that in the equality .a C b/ D .a/ C .b/ in the above definition, the addition on the left-hand side is taking place in the group G while the addition on the right-hand side is taking place in the group G 0 . Two sets play a critical role in understanding a group homomorphism W G ! G 0 . The first set is the image of , that is, the set .G/ D f.a/ W a 2 Gg. The second set is the kernel of , defined as the set of all elements of G that are mapped
142
Abelian groups
to 0G 0 by , that is, the set 1 .f0G 0 g/ D fa 2 G W .a/ D 0G 0 g. We introduce the following notation for these sets: Im denotes the image of , and Ker denotes the kernel of . Example 6.35. If H is a subgroup of an abelian group G, then the inclusion map i W H ! G is obviously a group homomorphism. Example 6.36. Suppose H is a subgroup of an abelian group G. We define the map W G ! G=H a 7! ŒaH : It is not hard to see that this is a group homomorphism. Indeed, this follows almost immediately from the way we defined addition in the quotient group G=H : .a C b/ D Œa C bH D ŒaH C ŒbH D .a/ C .b/: It is clear that is surjective. It is also not hard to see that Ker D H ; indeed, H is the identity element in G=H , and ŒaH D H if and only if a 2 H . The map is called the natural map from G to G=H . Example 6.37. For a given positive integer n, the natural map from Z to Zn sends a 2 Z to the residue class Œan . This map is a surjective group homomorphism with kernel nZ. Example 6.38. Suppose G is an abelian group and m is an integer. The map W G!G a 7! ma is a group homomorphism, since .a C b/ D m.a C b/ D ma C mb D .a/ C .b/: The image of this homomorphism is the subgroup mG and the kernel is the subgroup Gfmg. We call this map the m-multiplication map on G. If G is written multiplicatively, then this map, which sends a 2 G to am 2 G, is called the mpower map on G, and its image is G m . Example 6.39. Let p be an odd prime. Consider the 2-power, or squaring, map on Zp . Then as we saw in Example 6.21, the image .Zp /2 of this map is a subgroup of Zp of order .p 1/=2, and its kernel is Zp f2g D fŒ˙1g. Example 6.40. Consider the m-multiplication map on Z. As we saw in Example 6.22, its image mZ is equal to Z if and only if m D ˙1, while its kernel Zfmg is equal to Z if m D 0, and is equal to f0g otherwise.
6.4 Group homomorphisms and isomorphisms
143
Example 6.41. Consider the m-multiplication map on Zn . As we saw in Example 6.23, if d WD gcd.m; n/, the image mZn of this map is a subgroup of Zn of order n=d , while its kernel Zn fmg is a subgroup of order d . Example 6.42. Suppose G is an abelian group and a is an element of G. It is easy to see that the map W Z!G z 7! za is a group homomorphism, since .z C z 0 / D .z C z 0 /a D za C z 0 a D .z/ C .z 0 /: Example 6.43. As a special case of the previous example, let n be a positive integer and let ˛ be an element of Zn . Let W Z ! Zn be the group homomorphism that sends z 2 Z to ˛ z 2 Zn . That is a group homomorphism means 0 0 that ˛ zCz D ˛ z ˛ z for all z; z 0 2 Z (note that the group operation is addition in Z and multiplication in Zn ). If the multiplicative order of ˛ is equal to k, then as discussed in §2.7, the image of consists of the k distinct group elements ˛ 0 ; ˛ 1 ; : : : ; ˛ k 1 . The kernel of consists of those integers z such that ˛ z D 1. Again by the discussion in §2.7, the kernel of is equal to the subgroup kZ. Example 6.44. Generalizing Example 6.42, the reader may verify that if a1 ; : : : ; ak are fixed elements of an abelian group G, then the map Zk ! G
W
.z1 ; : : : ; zk / 7! z1 a1 C C zk ak is a group homomorphism. Example 6.45. Suppose that H1 ; : : : ; Hk are subgroups of an abelian group G. The reader may easily verify that the map W H1 Hk ! G .a1 ; : : : ; ak / 7! a1 C C ak is a group homomorphism whose image is the subgroup H1 C C Hk . The following theorem summarizes some of the most important properties of group homomorphisms. Theorem 6.19. Let be a group homomorphism from G to G 0 . (i) .0G / D 0G 0 . (ii) . a/ D
.a/ for all a 2 G.
144
Abelian groups
(iii) .na/ D n.a/ for all n 2 Z and a 2 G. (iv) If H is a subgroup of G, then .H / is a subgroup of G 0 ; in particular (setting H WD G), Im is a subgroup of G 0 . (v) If H 0 is a subgroup of G 0 , then 1 .H 0 / is a subgroup of G; in particular (setting H 0 WD f0G 0 g), Ker is a subgroup of G. (vi) For all a; b 2 G, .a/ D .b/ if and only if a b .mod Ker /. (vii) is injective if and only if Ker D f0G g. Proof. These are all straightforward calculations. (i) We have 0G 0 C .0G / D .0G / D .0G C 0G / D .0G / C .0G /: Now cancel .0G / from both sides. (ii) We have 0G 0 D .0G / D .a C . a// D .a/ C . a/; and hence . a/ is the inverse of .a/. (iii) For n D 0, this follows from part (i). For n > 0, this follows from the definitions by induction on n. For n < 0, this follows from the positive case and part (ii). (iv) For all a; b 2 H , we have a C b 2 H and a 2 H ; hence, .H / contains .a C b/ D .a/ C .b/ and . a/ D .a/. (v) 1 .H 0 / is non-empty, since .0G / D 00G 2 H 0 . If .a/ 2 H 0 and .b/ 2 H 0 , then .a C b/ D .a/ C .b/ 2 H 0 , and . a/ D .a/ 2 H 0 . (vi) We have .a/ D .b/ ” .a/ ” a
.b/ D 0G 0 ” .a
b/ D 0G 0
b 2 Ker ” a b .mod Ker /:
(vii) If is injective, then in particular, 1 .f0G 0 g/ cannot contain any other element besides 0G . If is not injective, then there exist two distinct elements a; b 2 G with .a/ D .b/, and by part (vi), Ker contains the element a b, which is non-zero. Part (vii) of the above theorem is particularly useful: to check that a group homomorphism is injective, it suffices to determine if Ker D f0G g. Thus, the injectivity and surjectivity of a given group homomorphism W G ! G 0 may be characterized in terms of its kernel and image: is injective if and only if Ker D f0G g;
6.4 Group homomorphisms and isomorphisms
145
is surjective if and only if Im D G 0 . We next present two very simple theorems that allow us to compose group homomorphisms in simple ways. Theorem 6.20. If W G ! G 0 and 0 W G 0 ! G 00 are group homomorphisms, then so is their composition 0 B W G ! G 00 . Proof. For all a; b 2 G, we have 0 ..a C b// D 0 ..a/ C .b// D 0 ..a// C 0 ..b//: Theorem 6.21. Let i W G ! Gi0 , for i D 1; : : : ; k, be group homomorphisms. Then the map W G ! G10 Gk0 a 7! .1 .a/; : : : ; k .a// is a group homomorphism. Proof. For all a; b 2 G, we have .a C b/ D .1 .a C b/; : : : ; k .a C b// D .1 .a/ C 1 .b/; : : : ; k .a/ C k .b// D .a/ C .b/: Consider a group homomorphism W G ! G 0 . If is bijective, then is called a group isomorphism of G with G 0 . If such a group isomorphism exists, we say that G is isomorphic to G 0 , and write G Š G 0 . Moreover, if G D G 0 , then is called a group automorphism on G. Theorem 6.22. If is a group isomorphism of G with G 0 , then the inverse function 1 is a group isomorphism of G 0 with G. Proof. For all a0 ; b 0 2 G 0 , we have . and hence
1
.a0 / C
1 .a0 /
C
1
.b 0 // D .
1 .b 0 /
D
1 .a0
1
.a0 // C .
1
.b 0 // D a0 C b 0 ;
C b 0 /.
Because of this theorem, if G is isomorphic to G 0 , we may simply say that “G and G 0 are isomorphic.” We stress that a group isomorphism of W G ! G 0 is essentially just a “renaming” of the group elements. This can be visualized as follows. Imagine the addition table for G written out with rows and columns labeled by elements of G, with the entry in row a and column b being a C b. Now suppose we use the function to consistently rename all the elements of G appearing in this table: the label on row a is replaced by .a/, the label on column b by .b/, and the entry in row a
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Abelian groups
and column b by .a C b/. Because is bijective, every element of G 0 appears exactly once as a label on a row and as a label on a column; moreover, because .a C b/ D .a/ C .b/, what we end up with is an addition table for G 0 . It follows that all structural properties of the group are preserved, even though the two groups might look quite different syntactically. Example 6.46. As was shown in Example 6.32, the quotient group G=H discussed in that example is isomorphic to Z3 . As was shown in Example 6.33, the quotient group Z15 =.Z15 /2 is isomorphic to Z2 Z2 . As was shown in Example 6.34, the quotient group Z5 =.Z5 /2 is isomorphic to Z2 . Example 6.47. If gcd.m; n/ D 1, then the m-multiplication map on Zn is a group automorphism. The next theorem tells us that corresponding to any group homomorphism, there is a natural group isomomorphism. As group isomorphisms are much nicer than group homomorphisms, this is often very useful. Theorem 6.23 (First isomorphism theorem). Let W G ! G 0 be a group homomorphism with kernel K and image H 0 . Then we have a group isomorphism G=K Š H 0 : Specifically, the map N W G=K ! G 0 ŒaK 7! .a/ is an injective group homomorphism whose image is H 0 . Proof. Using part (vi) of Theorem 6.19, we see that for all a; b 2 G, we have ŒaK D ŒbK ” a b .mod K/ ” .a/ D .b/: This immediately implies that the definition of N is unambiguous (since ŒaK D ŒbK implies .a/ D .b/), and that N is injective (since .a/ D .b/ implies ŒaK D ŒbK ). It is clear that N maps onto H 0 , since every element of H 0 is of the form .a/ for some a 2 G, and the map N sends ŒaK to .a/. Finally, to see that N is a group homomorphism, note that .Œa N K CŒbK / D .ŒaCb N N K /C .Œb N K /: K / D .aCb/ D .a/C.b/ D .Œa We can generalize the previous theorem, as follows: Theorem 6.24. Let W G ! G 0 be a group homomorphism. Then for every
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147
subgroup H of G with H Ker , we may define a group homomorphism N W G=H ! G 0 ŒaH 7! .a/: Moreover, Im N D Im , and N is injective if and only if H D Ker . Proof. Using the assumption that H Ker , we see that N is unambiguously defined, since for all a; b 2 G, we have ŒaH D ŒbH H) a b .mod H / H) a b .mod Ker / H) .a/ D .b/: That N is a group homomorphism, with Im N D Im , follows as in the proof of Theorem 6.23. If H D Ker , then by Theorem 6.23, N is injective, and if H ¨ Ker , then N is not injective, since if we choose a 2 Ker n H , we see that .Œa N H / D 0G 0 , and hence Ker N is non-trivial. The next theorem gives us another important construction of a group isomorphism. Theorem 6.25 (Internal direct product). Let G be an abelian group with subgroups H1 ; H2 , where H1 \ H2 D f0G g. Then we have a group isomorphism H1 H2 Š H1 C H2 given by the map W H1 H2 ! H1 C H2 .a1 ; a2 / 7! a1 C a2 : Proof. We already saw that is a surjective group homomorphism in Example 6.45. To see that is injective, it suffices to show that Ker is trivial; that is, it suffices to show that for all a1 2 H1 and a2 2 H2 , if a1 C a2 D 0G , then a1 D a2 D 0G . But a1 C a2 D 0G implies a1 D a2 2 H2 , and hence a1 2 H1 \ H2 D f0G g, and so a1 D 0G . Similarly, one shows that a2 D 0G , and that finishes the proof. If H1 ; H2 are as in the above theorem, then H1 C H2 is sometimes called the internal direct product of H1 and H2 . Example 6.48. We can use the general theory developed so far to get a quickand-dirty proof of the Chinese remainder theorem (Theorem 2.6). Let fni gkiD1 Q be a pairwise relatively prime family of positive integers, and let n WD kiD1 ni . Consider the map W Z ! Zn1 Znk a 7! .Œan1 ; : : : ; Œank /:
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Abelian groups
It is easy to see that this map is a group homomorphism; indeed, it is the map constructed in Theorem 6.21 applied with the natural maps i W Z ! Zni , for i D 1; : : : ; k. Evidently, a 2 Ker if and only if ni j a for i D 1; : : : ; k, and since fni gkiD1 is pairwise relatively prime, it follows that a 2 Ker if and only if n j a; that is, Ker D nZ. Theorem 6.23 then gives us an injective group homomorphism N W
Zn ! Zn1 Znk Œan 7! .Œan1 ; : : : ; Œank /:
But since the sets Zn and Zn1 Znk have the same size, injectivity implies surjectivity. From this, Theorem 2.6 is immediate. The map N is a group isomorphism Zn Š Zn1 Znk : In fact, the map N is the same as the map in Theorem 2.8, and so we also immediately obtain parts (i), (ii), (iii.a), and (iii.b) of Theorem 2.8. Observe that parts (iii.c) and (iii.d) of Theorem 2.8 imply that restricting the map to Zn yields an isomorphism of the multiplicative groups Zn Š Zn1 Znk : This fact does not follow from the general theory developed so far; however, in the next chapter, we will see how this fact fits into the broader algebraic picture. One advantage of our original proof of Theorem 2.6 is that it gives us an explicit formula for the inverse map 1 , which is useful in computations. Example 6.49. Let n1 ; n2 be positive integers with n1 j n2 . Consider the natural map W Z ! Zn1 . This is a surjective group homomorphism with Ker D n1 Z. Since H WD n2 Z n1 Z, we may apply Theorem 6.24 with the subgroup H , obtaining the surjective group homomorphism N W
Zn2 ! Zn1 Œan2 7! Œan1 :
Example 6.50. Let us revisit Example 6.23. Let n be a positive integer, and let m be any integer. Let 1 W Z ! Zn be the natural map, and let 2 W Zn ! Zn be the m-multiplication map. The composed map WD 2 B 1 from Z to Zn is also a group homomorphism. For each z 2 Z, we have .z/ D mŒzn D Œmzn . The kernel of consists of those integers z such that mz 0 .mod n/, and so Theorem 2.5 implies that Ker D .n=d /Z, where d WD gcd.m; n/. The image of
6.4 Group homomorphisms and isomorphisms
149
is mZn . Theorem 6.23 therefore implies that the map N W
Zn=d ! mZn Œzn=d 7! mŒzn
is a group isomorphism. Example 6.51. Consider the group Zp where p is an odd prime, and let W Zp ! Zp be the squaring map. By definition, Im D .Zp /2 , and we proved in Theorem 2.18 that Ker D fŒ˙1g. Theorem 2.19 says that for all ; ˇ 2 Zp ,
2 D ˇ 2 if and only if D ˙ˇ. This fact can also be seen to be a special case of part (vi) of Theorem 6.19. Theorem 6.23 says that Zp =Ker Š Im , and since jZp =Ker j D jZp j=jKer j D .p 1/=2, we see that Theorem 2.20, which says that j.Zp /2 j D .p 1/=2, follows from this. Let H WD .Zp /2 , and consider the quotient group Zp =H . Because jH j D .p 1/=2, we know that jZp =H j D jZp j=jH j D 2, and hence Zp =H consists of x WD Zp n H . the two cosets H and H x , and consider the map Let ˛ be an arbitrary, fixed element of H W Z ! Zp =H z 7! Œ˛ z H : It is easy to see that is a group homomorphism; indeed, it is the composition of the homomorphism discussed in Example 6.43 and the natural map from Zp to Zp =H . Moreover, it is easy to see (for example, as a special case of Theorem 2.17) that ˛ z 2 H ” z is even: From this, it follows that Ker D 2Z; also, since Zp =H consists of just the two x , it follows that is surjective. Therefore, Theorem 6.23 says that cosets H and H the map N W
Z2 ! Zp =H Œz2 7! Œ˛ z H
is a group isomorphism, under which Œ02 corresponds to H , and Œ12 corresponds x. to H This isomorphism gives another way to derive Theorem 2.23, which says that in Zp , the product of two non-squares is a square; indeed, the statement “non-zero plus non-zero equals zero in Z2 ” translates via the isomorphism N to the statement “non-square times non-square equals square in Zp .” Example 6.52. Let Q be the multiplicative group of non-zero rational numbers.
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Abelian groups
Let H1 be the subgroup f˙1g, and let H2 be the subgroup of positive rationals. It is easy to see that Q D H1 H2 and that H1 \ H2 D f1g. Thus, Q is the internal direct product of H1 and H2 , and Theorem 6.25 gives us a group isomorphism Q Š H1 H2 . Let G and G 0 be abelian groups. Recall from Example 6.19 that Map.G; G 0 / is the group of all functions W G ! G 0 , where the group operation is defined pointwise using the group operation of G 0 : . C /.a/ D .a/ C .a/ and . /.a/ D .a/ for all ; 2 Map.G; G 0 / and all a 2 G. The following theorem isolates an important subgroup of this group. Theorem 6.26. Let G and G 0 be abelian groups, and consider the group of functions Map.G; G 0 /. Then Hom.G; G 0 / WD f 2 Map.G; G 0 / W is a group homomorphismg is a subgroup of Map.G; G 0 /. Proof. First, observe that Hom.G; G 0 / is non-empty, as it contains the map that sends everything in G to 0G 0 (this is the identity element of Map.G; G 0 /). Next, we have to show that if and are homomorphisms from G to G 0 , then so are C and . But C D 2 B 1 , where 1 W G ! G 0 G 0 is the map constructed in Theorem 6.21, applied with and , and 2 W G 0 G 0 ! G 0 is as in Example 6.45. Also, D 1 B , where 1 is the . 1/-multiplication map. E XERCISE 6.22. Verify that the “is isomorphic to” relation on abelian groups is an equivalence relation; that is, for all abelian groups G1 ; G2 ; G3 , we have: (a) G1 Š G1 ; (b) G1 Š G2 implies G2 Š G1 ; (c) G1 Š G2 and G2 Š G3 implies G1 Š G3 . E XERCISE 6.23. Let i W Gi ! Gi0 , for i D 1; : : : ; k, be group homomorphisms. Show that the map W G1 Gk ! G10 Gk0 .a1 ; : : : ; ak / 7! .1 .a1 /; : : : ; k .ak // is a group homomorphism. Also show that if each i is an isomorphism, then so is . E XERCISE 6.24. Let W G ! G 0 be a group homomorphism. Let H; K be subgroups of G and let m be a positive integer. Show that .H C K/ D .H / C .K/ and .mH / D m.H /.
6.4 Group homomorphisms and isomorphisms
151
E XERCISE 6.25. Let W G ! G 0 be a group homomorphism. Let H be a subgroup of G, and let W H ! G 0 be the restriction of to H . Show that is a group homomorphism and that Ker D Ker \ H . E XERCISE 6.26. Suppose G1 ; : : : ; Gk are abelian groups. Show that for each i D 1; : : : ; k, the projection map i W G1 Gk ! Gi that sends .a1 ; : : : ; ak / to ai is a surjective group homomorphism. E XERCISE 6.27. Show that if G D G1 G2 for abelian groups G1 and G2 , and H1 is a subgroup of G1 and H2 is a subgroup of G2 , then we have a group isomorphism G=.H1 H2 / Š G1 =H1 G2 =H2 . E XERCISE 6.28. Let G be an abelian group with subgroups H and K. (a) Show that we have a group isomorphism .H C K/=K Š H=.H \ K/. (b) Show that if H and K are finite, then jH C Kj D jH jjKj=jH \ Kj. E XERCISE 6.29. Let G be an abelian group with subgroups H , K, and A, where K H . Show that .H \ A/=.K \ A/ is isomorphic to a subgroup of H=K. E XERCISE 6.30. Let W G ! G 0 be a group homomorphism with kernel K. Let H be a subgroup of G. Show that we have a group isomorphism G=.H C K/ Š .G/=.H /. E XERCISE 6.31. Let W G ! G 0 be a surjective group homomorphism. Let S be the set of all subgroups of G that contain Ker , and let S 0 be the set of all subgroups of G 0 . Show that the sets S and S 0 are in one-to-one correspondence, via the map that sends H 2 S to .H / 2 S 0 . Moreover, show that this correspondence preserves inclusions, that is, for all H1 ; H2 2 S, we have H1 H2 ” .H1 / .H2 /. E XERCISE 6.32. Use the previous exercise, together with Theorem 6.9, to get a short proof of Theorem 6.10. E XERCISE 6.33. Show that the homomorphism of Example 6.44 arises by direct application of Example 6.42, combined with Theorems 6.20 and 6.21. E XERCISE 6.34. Suppose that G, G1 , and G2 are abelian groups, and that W G1 G2 ! G is a group isomorphism. Let H1 WD .G1 f0G2 g/ and H2 WD .f0G1 g G2 /. Show that G is the internal direct product of H1 and H2 . E XERCISE 6.35. Let ZC denote the set of positive integers, and let Q be the multiplicative group of non-zero rational numbers. Consider the abelian groups Map# .ZC ; Z/ and Map# .ZC ; Z2 /, as defined in Exercise 6.14. Show that we have group isomorphisms
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Abelian groups
(a) Q Š Z2 Map# .ZC ; Z/, and (b) Q =.Q /2 Š Map# .ZC ; Z2 /. E XERCISE 6.36. Let n be an odd, positive integer whose factorization into primes is n D p1e1 prer . Show that: (a) we have a group isomorphism Zn =.Zn /2 Š Zr 2 ; (b) if pi 3 .mod 4/ for each i D 1; : : : ; r, then the squaring map on .Zn /2 is a group automorphism. E XERCISE 6.37. Which of the following pairs of groups are isomorphic? Why or why not? (a) Z2 Z2 and Z4 , (b) Z12 and Z8 , (c) Z5 and Z4 , (d) Z2 Z and Z, (e) Q and Z, (f) Z Z and Z. 6.5 Cyclic groups Let G be an abelian group. For a 2 G, define hai WD fza W z 2 Zg. It is easy to see that hai is a subgroup of G; indeed, it is the image of the group homomorphism discussed in Example 6.42. Moreover, hai is the smallest subgroup of G containing a; that is, hai contains a, and every subgroup H of G that contains a must also contain hai. Indeed, if a subgroup contains a, it must contain a C a D 2a, a C a C a D 3a, and so on; it must also contain 0G D 0a, a D . 1/a, . a/ C . a/ D . 2/a, and so on. The subgroup hai is called the subgroup (of G) generated by a. Also, one defines the order of a to be the order of the subgroup hai. More generally, for a1 ; : : : ; ak 2 G, we define ha1 ; : : : ; ak i WD fz1 a1 C C zk ak W z1 ; : : : ; zk 2 Zg: It is easy to see that ha1 ; : : : ; ak i is a subgroup of G; indeed, it is the image of the group homomorphism discussed in Example 6.44. Moreover, this subgroup is the smallest subgroup of G that contains a1 ; : : : ; ak ; that is, ha1 ; : : : ; ak i contains the elements a1 ; : : : ; ak , and any subgroup H that contains these elements must also contain ha1 ; : : : ; ak i. The subgroup ha1 ; : : : ; ak i it is called the subgroup (of G) generated by a1 ; : : : ; ak . An abelian group G is called cyclic if G D hai for some a 2 G, in which case, a is called a generator for G. An abelian group G is called finitely generated if G D ha1 ; : : : ; ak i for some a1 ; : : : ; ak 2 G. Multiplicative notation: if G is written multiplicatively, then hai WD faz W z 2 Zg, and ha1 ; : : : ; ak i WD fa1z1 akzk W z1 ; : : : ; zk 2 Zg; also, for emphasis and clarity, we use the term multiplicative order of a.
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6.5 Cyclic groups
Example 6.53. Consider the additive group Z. This is a cyclic group, with 1 being a generator: h1i D fz 1 W z 2 Zg D fz W z 2 Zg D Z: For every m 2 Z, we have hmi D fzm W z 2 Zg D fmz W z 2 Zg D mZ: It follows that the only elements of Z that generate Z are 1 and element generates a subgroup that is strictly contained in Z.
1: every other
Example 6.54. For n > 0, consider the additive group Zn . This is a cyclic group, with Œ1 being a generator: hŒ1i D fzŒ1 W z 2 Zg D fŒz W z 2 Zg D Zn : For every m 2 Z, we have hŒmi D fzŒm W z 2 Zg D fŒzm W z 2 Zg D fmŒz W z 2 Zg D mZn : By Example 6.23, the subgroup mZn has order n= gcd.m; n/. Thus, Œm has order n= gcd.m; n/; in particular, Œm generates Zn if and only if m is relatively prime to n, and hence, the number of generators of Zn is .n/. Implicit in Examples 6.53 and 6.54 is the following general fact: Theorem 6.27. Let G be a cyclic group generated by a. Then for every m 2 Z, we have hmai D mG: Proof. We have hmai D fz.ma/ W z 2 Zg D fm.za/ W z 2 Zg D mhai D mG: The following two examples present some groups that are not cyclic. Example 6.55. Consider the additive group G WD Z Z. Set ˛1 WD .1; 0/ 2 G and ˛2 WD .0; 1/ 2 G: It is not hard to see that G D h˛1 ; ˛2 i, since for all z1 ; z2 2 Z, we have z1 ˛1 C z2 ˛2 D .z1 ; 0/ C .0; z2 / D .z1 ; z2 /: However, G is not cyclic. To see this, let ˇ D .b1 ; b2 / be an arbitrary element of G. We claim that one of ˛1 or ˛2 does not belong to hˇi. Suppose to the contrary
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Abelian groups
that both ˛1 and ˛2 belong to hˇi. This would imply that there exist integers z and z 0 such that zb1 D 1;
zb2 D 0;
0
z 0 b2 D 1:
z b1 D 0;
Multiplying the upper left equality by the lower right, and the upper right by the lower left, we obtain 1 D zz 0 b1 b2 D 0; which is impossible. Example 6.56. Consider the additive group G WD Zn1 Zn2 . Set ˛1 WD .Œ1n1 ; Œ0n2 / 2 G and ˛2 WD .Œ0n1 ; Œ1n2 / 2 G: It is not hard to see that G D h˛1 ; ˛2 i, since for all z1 ; z2 2 Z, we have z1 ˛1 C z2 ˛2 D .Œz1 n1 ; Œ0n2 / C .Œ0n1 ; Œz2 n2 / D .Œz1 n1 ; Œz2 n2 /: However, G may or may not be cyclic: it depends on d WD gcd.n1 ; n2 /. If d D 1, then G is cyclic, with ˛ WD .Œ1n1 ; Œ1n2 / being a generator. One can see this easily using the Chinese remainder theorem: for all z1 ; z2 2 Z, there exists z 2 Z such that z z1 .mod n1 / and z z2 .mod n2 /; which implies z˛ D .Œzn1 ; Œzn2 / D .Œz1 n1 ; Œz2 n2 /: If d > 1, then G is not cyclic. To see this, let ˇ D .Œb1 n1 ; Œb2 n2 / be an arbitrary element of G. We claim that one of ˛1 or ˛2 does not belong to hˇi. Suppose to the contrary that both ˛1 and ˛2 belong to hˇi. This would imply that there exist integers z and z 0 such that zb1 1 .mod n1 /; 0
z b1 0 .mod n1 /;
zb2 0 .mod n2 /; z 0 b2 1 .mod n2 /:
All of these congruences hold modulo d as well, and multiplying the upper left congruence by the lower right, and the upper right by the lower left, we obtain 1 zz 0 b1 b2 0 .mod d /; which is impossible.
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155
It should be clear that since a group isomorphism preserves all structural properties of groups, it preserves the property of being cyclic. We state this, along with related facts, as a theorem. Theorem 6.28. Let W G ! G 0 be a group isomorphism. (i) For all a 2 G, we have .hai/ D h.a/i. (ii) For all a 2 G, a and .a/ have the same order. (iii) G is cyclic if and only if G 0 is cyclic. Proof. For all a 2 G, we have .hai/ D f.za/ W z 2 Zg D fz.a/ W z 2 Zg D h.a/i: That proves (i). (ii) follows from (i) and the fact that is injective. (iii) follows from (i), as follows. If G is cyclic, then G D hai, and since is surjective, we have G 0 D .G/ D h.a/i. The converse follows by applying the same argument to the inverse isomorphism 1 W G 0 ! G. Example 6.57. Consider again the additive group G WD Zn1 Zn2 , discussed in Example 6.56. If gcd.n1 ; n2 / D 1, then one can also see that G is cyclic as follows: by the discussion in Example 6.48, we know that G is isomorphic to Zn1 n2 , and since Zn1 n2 is cyclic, so is G. Example 6.58. Consider again the subgroup mZn of Zn , discussed in Example 6.54. One can also see that this is cyclic of order n=d , where d WD gcd.m; n/, as follows: in Example 6.50, we constructed an isomorphism between Zn=d and mZn , and this implies mZn is cyclic of order n=d . Classification of cyclic groups. Examples 6.53 and 6.54 are extremely important examples of cyclic groups. Indeed, as we shall now demonstrate, every cyclic group is isomorphic either to Z or to Zn for some n > 0. Suppose that G is a cyclic group with generator a. Consider the map W Z ! G that sends z 2 Z to za 2 G. As discussed in Example 6.42, this map is a group homomorphism, and since a is a generator for G, it must be surjective. There are two cases to consider. Case 1: Ker D f0g. In this case, is an isomorphism of Z with G. Case 2: Ker ¤ f0g. In this case, since Ker is a subgroup of Z different from f0g, by Theorem 6.9, it must be of the form nZ for some n > 0. Hence, by Theorem 6.23, the map N W Zn ! G that sends Œzn to za is an isomorphism of Zn with G. Based on the this isomorphism, we immediately obtain:
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Abelian groups
Theorem 6.29. Let G be an abelian group and let a 2 G. If there exists a positive integer m such that ma D 0G , then the least such positive integer n is the order of a; in this case, we have: for every integer z, za D 0G if and only if n divides z, and more generally, for all integers z1 ; z2 , we have z1 a D z2 a if and only if z1 z2 .mod n/; the subgroup hai consists of the n distinct elements 0 a; 1 a; : : : ; .n
1/ a:
Otherwise, a has infinite order, and every element of hai can be expressed as za for some unique integer z. In the case where the group is finite, we can say more: Theorem 6.30. Let G be a finite abelian group and let a 2 G. Then jGja D 0G and the order of a divides jGj. Proof. Since hai is a subgroup of G, by Lagrange’s theorem (Theorem 6.15), the order of a divides jGj. It then follows by Theorem 6.29 that jGja D 0G . Example 6.59. Let a; n 2 Z with n > 0 and gcd.a; n/ D 1, and let ˛ WD Œa 2 Zn . Theorem 6.29 implies that the definition given in this section of the multiplicative order of ˛ is consistent with that given in §2.7. Moreover, Euler’s theorem (Theorem 2.13) can be seen as just a special case of Theorem 6.30. Also, note that ˛ is a generator for Zn if and only if a is a primitive root modulo p. Example 6.60. As we saw in Example 6.26, all elements of Z15 have multiplicative order dividing 4, and since Z15 has order 8, we conclude that Z15 is not cyclic. Example 6.61. The group Z5 is cyclic, with Œ2 being a generator: Œ22 D Œ4 D Œ 1; Œ23 D Œ 2; Œ24 D Œ1: Example 6.62. Based on the calculations in Example 2.8, we may conclude that Z7 is cyclic, with both Œ3 and Œ5 being generators. Example 6.63. Consider again the additive group G WD Zn1 Zn2 , discussed in Example 6.56. If d WD gcd.n1 ; n2 / > 1, then one can also see that G is not cyclic as follows: for every ˇ 2 G, we have .n1 n2 =d /ˇ D 0G , and hence by Theorem 6.29, the order of ˇ divides n1 n2 =d . The following two theorems completely characterize the subgroup structure of cyclic groups. Actually, we have already proven most of the results in these two theorems, but nevertheless, they deserve special emphasis.
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157
Theorem 6.31. Let G be a cyclic group of infinite order. (i) G is isomorphic to Z. (ii) The subgroups of G are in one-to-one correspondence with the nonnegative integers, where each such integer m corresponds to the cyclic group mG. (iii) For every two non-negative integers m; m0 , mG m0 G if and only if m0 j m. Proof. That G Š Z was established in our classification of cyclic groups, and so it suffices to prove the other statements of the theorem for G D Z. As we saw in Example 6.53, for every integer m, the subgroup mZ is cyclic, as it is generated by m. This fact, together with Theorem 6.9, establishes all the other statements. Theorem 6.32. Let G be a cyclic group of finite order n. (i) G is isomorphic to Zn . (ii) The subgroups of G are in one-to-one correspondence with the positive divisors of n, where each such divisor d corresponds to the subgroup dG; moreover, dG is a cyclic group of order n=d . (iii) For each positive divisor d of n, we have dG D Gfn=d g; that is, the kernel of the .n=d /-multiplication map is equal to the image of the d multiplication map; in particular, Gfn=d g has order n=d . (iv) For every two positive divisors d; d 0 of n, we have dG d 0 G if and only if d 0 j d . (v) For every positive divisor d of n, the number of elements of order d in G is .d /. (vi) For every integer m, we have mG D dG and Gfmg D Gfd g, where d WD gcd.m; n/. Proof. That G Š Zn was established in our classification of cyclic groups, and so it suffices to prove the other statements of the theorem for G D Zn . The one-to-one correspondence in part (ii) was established in Theorem 6.10. By the discussion in Example 6.54, it is clear that d Zn is generated by Œd and has order n=d . Part (iii) was established in Example 6.23. Part (iv) was established in Theorem 6.10. For part (v), the elements of order d in Zn are all contained in Zn fd g, and so the number of such elements is equal to the number of generators of Zn fd g. The group Zn fd g is cyclic of order d , and so is isomorphic to Zd , and as we saw in Example 6.54, this group has .d / generators.
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Abelian groups
Part (vi) was established in Example 6.23. Since cyclic groups are in some sense the simplest kind of abelian group, it is nice to establish some sufficient conditions under which a group must be cyclic. The following three theorems provide such conditions. Theorem 6.33. If G is an abelian group of prime order, then G is cyclic. Proof. Let jGj D p. Let a 2 G with a ¤ 0G , and let k be the order of a. As the order of an element divides the order of the group, we have k j p, and so k D 1 or k D p. Since a ¤ 0G , we must have k ¤ 1, and so k D p, which implies that a generates G. Theorem 6.34. If G1 and G2 are finite cyclic groups of relatively prime order, then G1 G2 is also cyclic. In particular, if G1 is generated by a1 and G2 is generated by a2 , then G1 G2 is generated by .a1 ; a2 /. Proof. We give a direct proof, based on Theorem 6.29. Let n1 WD jG1 j and n2 WD jG2 j, where gcd.n1 ; n2 / D 1. Also, let a1 2 G1 have order n1 and a2 2 G2 have order n2 . We want to show that .a1 ; a2 / has order n1 n2 . Applying Theorem 6.29 to .a1 ; a2 /, we see that the order of .a1 ; a2 / is the smallest positive integer k such that k.a1 ; a2 / D .0G1 ; 0G2 /. Now, for every integer k, we have k.a1 ; a2 / D .ka1 ; ka2 /, and .ka1 ; ka2 / D .0G1 ; 0G2 / ” n1 j k and n2 j k (applying Theorem 6.29 to a1 and a2 ) ” n1 n2 j k (since gcd.n1 ; n2 / D 1): Theorem 6.35. Let G be a cyclic group. Then for every subgroup H of G, both H and G=H are cyclic. Proof. The fact that H is cyclic follows from part (ii) of Theorem 6.31 in the case where G is infinite, and part (ii) of Theorem 6.32 in the case where G is finite. If G is generated by a, then it is easy to see that G=H is generated by ŒaH . The next three theorems are often useful in calculating the order of a group element. The first generalizes Theorem 2.15. Theorem 6.36. Let G be an abelian group, let a 2 G be of finite order n, and let m be an arbitrary integer. Then the order of ma is n= gcd.m; n/. Proof. Let H WD hai, and d WD gcd.m; n/. By Theorem 6.27, we have hmai D mH , and by Theorem 6.32, we have mH D dH , which has order n=d . That proves the theorem. Alternatively, we can give a direct proof, based on Theorem 6.29. Applying Theorem 6.29 to ma, we see that order of ma is the
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smallest positive integer k such that k.ma/ D 0G . Now, for every integer k, we have k.ma/ D .km/a, and .km/a D 0G ” km 0 .mod n/ (applying Theorem 6.29 to a) ” k 0 .mod n= gcd.m; n// (by Theorem 2.5): Theorem 6.37. Suppose that a is an element of an abelian group, and for some prime p and integer e 1, we have p e a D 0G and p e 1 a ¤ 0G . Then a has order p e . Proof. If m is the order of a, then since p e a D 0G , we have m j p e . So m D p f for some f D 0; : : : ; e. If f < e, then p e 1 a D 0G , contradicting the assumption that p e 1 a ¤ 0G . Theorem 6.38. Suppose G is an abelian group with a1 ; a2 2 G such that a1 is of finite order n1 , a2 is of finite order n2 , and gcd.n1 ; n2 / D 1. Then the order of a1 C a2 is n1 n2 . Proof. Let H1 WD ha1 i and H2 WD ha2 i so that jH1 j D n1 and jH2 j D n2 . First, we claim that H1 \ H2 D f0G g. To see this, observe that H1 \ H2 is a subgroup of H1 , and so jH1 \ H2 j divides n1 ; similarly, jH1 \ H2 j divides n2 . Since gcd.n1 ; n2 / D 1, we must have jH1 \ H2 j D 1, and that proves the claim. Using the claim, we can apply Theorem 6.25, obtaining a group isomorphism between H1 C H2 and H1 H2 . Under this isomorphism, the group element a1 C a2 2 H1 C H2 corresponds to .a1 ; a2 / 2 H1 H2 , which by Theorem 6.34 (again using the fact that gcd.n1 ; n2 / D 1) has order n1 n2 . For an abelian group G, we say that an integer k kills G if kG D f0G g. Consider the set KG of integers that kill G. Evidently, KG is a subgroup of Z, and hence of the form mZ for a uniquely determined non-negative integer m. This integer m is called the exponent of G. If m ¤ 0, then we see that m is the least positive integer that kills G. We first state some basic properties. Theorem 6.39. Let G be an abelian group of exponent m. (i) For every integer k such that kG D f0G g, we have m j k. (ii) If G has finite order, then m divides jGj. (iii) If m ¤ 0, then for every a 2 G, the order of a is finite, and the order of a divides m. (iv) If G is cyclic, then the exponent of G is 0 if G is infinite, and is jGj if G is finite.
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Proof. Exercise. The next two theorems develop some crucial properties about the structure of finite abelian groups. Theorem 6.40. If an abelian group G has non-zero exponent m, then G contains an element of order m. In particular, a finite abelian group is cyclic if and only if its order equals its exponent. Proof. The second statement follows immediately from the first. For the first stateQ ment, assume that m > 1, and let m D riD1 piei be the prime factorization of m. First, we claim that for each i D 1; : : : ; r, there exists ai 2 G such that .m=pi /ai ¤ 0G . Suppose the claim were false: then for some i, .m=pi /a D 0G for all a 2 G; however, this contradicts the minimality property in the definition of the exponent m. That proves the claim. Let a1 ; : : : ; ar be as in the above claim. Then by Theorem 6.37, .m=piei /ai has order piei for each i D 1; : : : ; r. Finally, by Theorem 6.38, the group element .m=p1e1 /a1 C C .m=prer /ar has order m. Theorem 6.41. Let G be a finite abelian group of order n. If p is a prime dividing n, then G contains an element of order p. Proof. We can prove this by induction on n. If n D 1, then the theorem is vacuously true. Now assume n > 1 and that the theorem holds for all groups of order strictly less than n. Let a be any non-zero element of G, and let m be the order of a. Since a is non-zero, we must have m > 1. If p j m, then .m=p/a is an element of order p, and we are done. So assume that p − m and consider the quotient group G=H , where H is the subgroup of G generated by a. Since H has order m, G=H has order n=m, which is strictly less than n, and since p − m, we must have p j .n=m/. So we can apply the induction hypothesis to the group G=H and the prime p, which says that there is an element b 2 G such that the coset ŒbH 2 G=H has order p. If ` is the order of b, then `b D 0G , and so `b 0G .mod H /, which implies that the order of ŒbH divides `. Thus, p j `, and so .`=p/b is an element of G of order p. As a corollary, we have: Theorem 6.42. Let G be a finite abelian group. Then the primes dividing the exponent of G are the same as the primes dividing its order.
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Proof. Since the exponent divides the order, every prime dividing the exponent must divide the order. Conversely, if a prime p divides the order, then since there is an element of order p in the group, the exponent must be divisible by p. E XERCISE 6.38. Find ˛1 ; ˛2 2 Z15 such that Z15 D h˛1 ; ˛2 i. E XERCISE 6.39. Show that Q is not finitely generated. E XERCISE 6.40. Let G be an abelian group, a 2 G, and m 2 Z, such that m > 0 and ma D 0G . Let m D p1e1 prer be the prime factorization of m. For i D 1; : : : ; r, let fi be the largest non-negative integer such that fi ei and f e f e f m=pi i a D 0G . Show that the order of a is equal to p11 1 pr r r . E XERCISE 6.41. Let G be an abelian group of order n, and let m be an integer. Show that mG D G if and only if gcd.m; n/ D 1. E XERCISE 6.42. Let H be a subgroup of an abelian group G. Show that: (a) if H and G=H are both finitely generated, then so is G; (b) if G is finite, gcd.jH j; jG=H j/ D 1, and H and G=H are both cyclic, then G is cyclic. E XERCISE 6.43. Show that for abelian groups G1 ; G2 whose exponents are m1 and m2 , the exponent of G1 G2 is lcm.m1 ; m2 /. E XERCISE 6.44. Let G be an abelian group of exponent m1 m2 , where gcd.m1 ; m2 / D 1. Show that G is the internal direct product of m1 G and m2 G. E XERCISE 6.45. Show how Theorem 2.40 easily follows from Theorem 6.32. E XERCISE 6.46. Consider the quotient group G WD Q=Z. Show that: (a) G has exponent 0; (b) all elements of G have finite order; (c) for all positive integers m, we have mG D G and Gfmg Š Zm ; (d) all finite subgroups of G are cyclic. E XERCISE 6.47. Suppose that G is an abelian group that satisfies the following properties: (i) for all m 2 Z, Gfmg is either equal to G or is of finite order; (ii) for some m 2 Z, f0G g ¨ Gfmg ¨ G. Show that Gfmg is finite for all non-zero m 2 Z.
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6.6 The structure of finite abelian groups ./ We next state a theorem that classifies all finite abelian groups up to isomorphism. Theorem 6.43 (Fundamental theorem of finite abelian groups). A finite abelian group (with more than one element) is isomorphic to a direct product of cyclic groups Zpe1 Zprer ; 1
where the pi ’s are primes (not necessarily distinct) and the ei ’s are positive integers. This direct product of cyclic groups is unique up to the order of the factors. An alternative statement of this theorem is the following: Theorem 6.44. A finite abelian group (with more than one element) is isomorphic to a direct product of cyclic groups Zm1 Zm t ; where each mi > 1, and where for i D 1; : : : ; t 1, we have mi j mi C1 . Moreover, the integers m1 ; : : : ; m t are uniquely determined, and m t is the exponent of the group. The statements of these theorems are much more important than their proofs. Even if the reader does not study the proofs, he is urged to understand what the theorems actually say. In an exercise below, you are asked to show that these two theorems are equivalent. We now prove Theorem 6.44, which we break into two lemmas, the first of which proves the existence part of the theorem, and the second of which proves the uniqueness part. Lemma 6.45. A finite abelian group (with more than one element) is isomorphic to a direct product of cyclic groups Zm1 Zm t ; where each mi > 1, and where for i D 1; : : : ; t m t is the exponent of the group.
1, we have mi j mi C1 ; moreover,
Proof. Let G be a finite abelian group with more than one element, and let m be the exponent of G. By Theorem 6.40, there exists an element a 2 G of order m. Let A D hai. Then A Š Zm . Now, if A D G, the lemma is proved. So assume that A ¨ G. We will show that there exists a subgroup B of G such that G D A C B and A \ B D f0G g. From this, Theorem 6.25 gives us an isomorphism of G with
6.6 The structure of finite abelian groups ./
163
A B. Moreover, the exponent of B is clearly a divisor of m, and so the lemma will follow by induction (on the order of the group). So it suffices to show the existence of a subgroup B as above. We prove this by contradiction. Suppose that there is no such subgroup, and among all subgroups B such that A \ B D f0G g, assume that B is maximal, meaning that there is no subgroup B 0 of G such that B ¨ B 0 and A \ B 0 D f0G g. By assumption C WD A C B ¨ G. Let d be any element of G that lies outside of C . Consider the quotient group G=C , and let r be the order of Œd C 2 G=C . Note that r > 1 and r j m. We shall define a group element d 0 with slightly nicer properties than d , as follows. Since rd 2 C , we have rd D sa C b for some s 2 Z and b 2 B. We claim that r j s. To see this, note that 0G D md D .m=r/rd D .m=r/sa C .m=r/b, and since A \ B D f0G g, we have .m=r/sa D 0G , which can only happen if r j s. That proves the claim. This allows us to define d 0 WD d .s=r/a. Since d d 0 .mod C /, we see not only that Œd 0 C 2 G=C has order r, but also that rd 0 2 B. We next show that A\.B Chd 0 i/ D f0G g, which will yield the contradiction we seek, and thus prove the lemma. Because A\B D f0G g, it will suffice to show that A \ .B C hd 0 i/ B. Now, suppose we have a group element b 0 C xd 0 2 A, with b 0 2 B and x 2 Z. Then in particular, xd 0 2 C , and so r j x, since Œd 0 C 2 G=C has order r. Further, since rd 0 2 B, we have xd 0 2 B, whence b 0 C xd 0 2 B. Lemma 6.46. Suppose that G WD Zm1 Zm t and H WD Zn1 Zn t are isomorphic, where the mi ’s and ni ’s are positive integers (possibly 1) such that mi j mi C1 and ni j ni C1 for i D 1; : : : ; t 1. Then mi D ni for i D 1; : : : ; t . Q Q Proof. Clearly, i mi D jGj D jH j D i ni . We prove the lemma by induction on the order of the group. If the group order is 1, then clearly all the mi ’s and ni ’s must be 1, and we are done. Otherwise, let p be a prime dividing the group order. Now, suppose that p divides mr ; : : : ; m t but not m1 ; : : : ; mr 1 , and that p divides ns ; : : : ; n t but not n1 ; : : : ; ns 1 , where r t and s t. Evidently, the groups pG and pH are isomorphic. Moreover, pG Š Zm1 Zmr
1
Zmr =p Zm t =p ;
pH Š Zn1 Zns
1
Zns =p Zn t =p :
and Thus, we see that jpGj D jGj=p t rC1 and jpH j D jH j=p t sC1 , from which it follows that r D s, and the lemma then follows by induction.
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E XERCISE 6.48. Show that Theorems 6.43 and 6.44 are equivalent; that is, show that each one implies the other. To do this, give a natural one-to-one correspondence between sequences of prime powers (as in Theorem 6.43) and sequences of integers m1 ; : : : ; m t (as in Theorem 6.44). E XERCISE 6.49. Using the fundamental theorem of finite abelian groups (either form), give short and simple proofs of Theorems 6.40 and 6.41. E XERCISE 6.50. In our proof of Euler’s criterion (Theorem 2.21), we really only used the fact that Zp has a unique element of multiplicative order 2. This exercise develops a proof of a generalization of Euler’s criterion, based on the fundamental theorem of finite abelian groups. Suppose G is an abelian group of even order n that contains a unique element of order 2. (a) Show that G Š Z2e Zm1 Zmk , where e > 0 and the mi ’s are odd integers. (b) Using part (a), show that 2G D Gfn=2g. E XERCISE 6.51. Let G be a non-trivial, finite abelian group. Let s be the smallest positive integer with the following property: G D ha1 ; : : : ; as i for some a1 ; : : : ; as 2 G. Show that s is equal to the value of t in Theorem 6.44. In particular, G is cyclic if and only if t D 1. E XERCISE 6.52. Suppose G Š Zm1 Zm t . Let p be a prime, and let s be the number of mi ’s divisible by p. Show that Gfpg Š Zps . E XERCISE 6.53. Suppose G Š Zm1 Zm t with mi j mi C1 for i D 1; : : : ; t 1, and that H is a subgroup of G. Show that H Š Zn1 Zn t , where ni j ni C1 for i D 1; : : : ; t 1 and ni j mi for i D 1; : : : ; t . E XERCISE 6.54. Suppose that G is an abelian group such that for all m > 0, we have mG D G and jGfmgj D m2 (note that G is not finite). Show that Gfmg Š Zm Zm for all m > 0. Hint: use induction on the number of prime factors of m.
7 Rings
This chapter introduces the notion of a ring, more specifically, a commutative ring with unity. The theory of rings provides a useful conceptual framework for reasoning about a wide class of interesting algebraic structures. Intuitively speaking, a ring is an algebraic structure with addition and multiplication operations that behave as one would expect. While there is a lot of terminology associated with rings, the basic ideas are fairly simple. 7.1 Definitions, basic properties, and examples Definition 7.1. A commutative ring with unity is a set R together with addition and multiplication operations on R, such that: (i) the set R under addition forms an abelian group, and we denote the additive identity by 0R ; (ii) multiplication is associative; that is, for all a; b; c 2 R, we have a.bc/ D .ab/c; (iii) multiplication distributes over addition; that is, for all a; b; c 2 R, we have a.b C c/ D ab C ac and .b C c/a D ba C ca; (iv) there exists a multiplicative identity; that is, there exists an element 1R 2 R, such that 1R a D a D a 1R for all a 2 R; (v) multiplication is commutative; that is, for all a; b 2 R, we have ab D ba. There are other, more general (and less convenient) types of rings — one can drop properties (iv) and (v), and still have what is called a ring. We shall not, however, be working with such general rings in this text. Therefore, to simplify terminology, from now on, by a “ring,” we shall always mean a commutative ring with unity. Let R be a ring. Notice that because of the distributive law, for any fixed a 2 R, 165
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the map from R to R that sends b 2 R to ab 2 R is a group homomorphism with respect to the underlying additive group of R. We call this the a-multiplication map. We first state some simple facts: Theorem 7.2. Let R be a ring. Then: (i) the multiplicative identity 1R is unique; (ii) 0R a D 0R for all a 2 R; (iii) . a/b D a. b/ D
.ab/ for all a; b 2 R;
(iv) . a/. b/ D ab for all a; b 2 R; (v) .ka/b D a.kb/ D k.ab/ for all k 2 Z and a; b 2 R. Proof. Part (i) may be proved using the same argument as was used to prove part (i) of Theorem 6.2. Parts (ii), (iii), and (v) follow directly from parts (i), (ii), and (iii) of Theorem 6.19, using appropriate multiplication maps, discussed above. Part (iv) follows from part (iii), along with part (iv) of Theorem 6.3: . a/. b/ D .a. b// D . .ab// D ab. Example 7.1. The set Z under the usual rules of multiplication and addition forms a ring. Example 7.2. For n 1, the set Zn under the rules of multiplication and addition defined in §2.5 forms a ring. Example 7.3. The set Q of rational numbers under the usual rules of multiplication and addition forms a ring. Example 7.4. The set R of real numbers under the usual rules of multiplication and addition forms a ring. Example 7.5. The set C of complex numbers under the usual rules of multiplication and addition forms a ring. Every (uniquely) as p ˛ 2 0C can0 be written 0 ˛ D a C bi, where a; b 2 R and i D 1. If ˛ D a C b i is another complex number, with a0 ; b 0 2 R, then ˛ C ˛ 0 D .a C a0 / C .b C b 0 /i and ˛˛ 0 D .aa0
bb 0 / C .ab 0 C a0 b/i:
The fact that C is a ring can be verified by direct calculation; however, we shall see later that this follows easily from more general considerations. Recall the complex conjugation operation, which sends ˛ to ˛N WD a bi. One can verify by direct calculation that complex conjugation is both additive and multiplicative; that is, ˛ C ˛ 0 D ˛N C ˛N 0 and ˛ ˛ 0 D ˛N ˛N 0 . The norm of ˛ is N.˛/ WD ˛ ˛N D a2 Cb 2 . So we see that N.˛/ is a non-negative
7.1 Definitions, basic properties, and examples
167
real number, and is zero if and only if ˛ D 0. Moreover, from the multiplicativity of complex conjugation, it is easy to see that the norm is multiplicative as well: N.˛˛ 0 / D ˛˛ 0 ˛˛ 0 D ˛˛ 0 ˛N ˛N 0 D N.˛/N.˛ 0 /. Example 7.6. Consider the set F of all arithmetic functions, that is, functions mapping positive integers to reals. Let us define addition of arithmetic functions point-wise (i.e., .f C g/.n/ D f .n/ C g.n/ for all positive integers n) and multiplication using the Dirichlet product, introduced in §2.9. The reader should verify that with addition and multiplication so defined, F forms a ring, where the all-zero function is the additive identity, and the special function ı defined in §2.9 is the multiplicative identity. Example 7.7. Generalizing Example 6.18, if R1 ; : : : ; Rk are rings, then we can form the direct product S WD R1 Rk , which consists of all k-tuples .a1 ; : : : ; ak / with a1 2 R1 ; : : : ; ak 2 Rk . We can view S in a natural way as a ring, with addition and multiplication defined component-wise. The additive identity is .0R1 ; : : : ; 0Rk / and the multiplicative identity is .1R1 ; : : : ; 1Rk /. When R D R1 D D Rk , the k-wise direct product of R is denoted Rk . Example 7.8. Generalizing Example 6.19, if I is an arbitrary set and R is a ring, then Map.I; R/, which is the set of all functions f W I ! R, may be naturally viewed as a ring, with addition and multiplication defined point-wise; that is, for f; g 2 Map.I; R/, we define .f C g/.i/ WD f .i / C g.i / and .f g/.i / WD f .i / g.i / for all i 2 I . We leave it to the reader to verify that Map.I; R/ is a ring, where the additive identity is the all-zero function, and the multiplicative identity is the all-one function. Note that in a ring R, if 1R D 0R , then for all a 2 R, we have a D 1R a D 0R a D 0R , and hence the ring R is trivial, in the sense that it consists of the single element 0R , with 0R C 0R D 0R and 0R 0R D 0R . If 1R ¤ 0R , we say that R is non-trivial. We shall rarely be concerned with trivial rings for their own sake; however, they do sometimes arise in certain constructions. For a1 ; : : : ; ak ; b1 ; : : : ; b` 2 R, the distributive law implies X .a1 C C ak /.b1 C C b` / D ai bj : 1i k 1j `
For a1 ; : : : ; ak 2 R, the product a1 ak needs no parentheses, because multiplication is associative; moreover, we can reorder the ai ’s without changing the value of the product, since multiplication is commutative. We can also write this
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Qk
product as i D1 ai . By convention, such a product is defined to be 1R when k D 0. When a D a1 D D ak , we can write this product as ak . The reader may verify the usual power laws: for all a; b 2 R, and all non-negative integers k and `, we have .a` /k D ak` D .ak /` ; akC` D ak a` ; .ab/k D ak b k :
(7.1)
A ring R is in particular an abelian group with respect to addition. We shall call a subgroup of the additive group of R an additive subgroup of R. The characteristic of R is defined as the exponent of this group (see §6.5). Note that for all m 2 Z and a 2 R, we have ma D m.1R a/ D .m 1R /a; so that if m 1R D 0R , then ma D 0R for all a 2 R. Thus, if the additive order of 1R is infinite, the characteristic of R is zero, and otherwise, the characteristic of R is equal to the additive order of 1R . Example 7.9. The ring Z has characteristic zero, Zn has characteristic n, and Zn1 Zn2 has characteristic lcm.n1 ; n2 /. When there is no possibility for confusion, one may write “0” instead of “0R ” and “1” instead of “1R .” Also, one may also write, for example, 2R to denote 21R , 3R to denote 3 1R , and so on; moreover, where the context is clear, one may use an implicit “type cast,” so that m 2 Z really means m 1R . E XERCISE 7.1. Show that the familiar binomial theorem (see §A2) holds in an arbitrary ring R; that is, for all a; b 2 R and every positive integer n, we have ! n X n n k k n a b : .a C b/ D k kD0
E XERCISE 7.2. Let R be a ring. For additive subgroups A and B of R, we define their ring-theoretic product AB as the set of all elements of R that can be expressed as a1 b1 C C ak bk for some a1 ; : : : ; ak 2 A and b1 ; : : : ; bk 2 B; by definition, this set includes the “empty sum” 0R . Show that for all additive subgroups A; B; and C of R: (a) AB is also an additive subgroup of R; (b) AB D BA; (c) A.BC / D .AB/C ; (d) A.B C C / D AB C AC .
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169
7.1.1 Divisibility, units, and fields For elements a; b in a ring R, we say that a divides b if ar D b for some r 2 R. If a divides b, we write a j b, and we may say that a is a divisor of b, or that b is a multiple of a, or that b is divisible by a. If a does not divide b, then we write a − b. Note that Theorem 1.1 holds for an arbitrary ring. We call a 2 R a unit if a j 1, that is, if ar D 1 for some r 2 R. Using the same argument as was used to prove part (ii) of Theorem 6.2, it is easy to see that r is uniquely determined; it is called the multiplicative inverse of a, and we denote it by a 1 . Also, for b 2 R, we may write b=a to denote ba 1 . Evidently, if a is a unit, then a j b for every b 2 R. We denote the set of units by R . It is not hard to see that the set R is closed under multiplication, from which it follows that R is an abelian group, called the multiplicative group of units of R. If a 2 R , then of course ak 2 R for all non-negative integers k, and the multiplicative inverse of ak is .a 1 /k , which we may also write as a k (which is consistent with our notation for abelian groups). For all a; b 2 R , the identities (7.1) hold for all integers k and `. If R is non-trivial and every non-zero element of R has a multiplicative inverse, then R is called a field. Example 7.10. The only units in the ring Z are ˙1. Hence, Z is not a field. Example 7.11. Let n be a positive integer. The units in Zn are the residue classes Œan with gcd.a; n/ D 1. In particular, if n is prime, all non-zero residue classes are units, and if n is composite, some non-zero residue classes are not units. Hence, Zn is a field if and only if n is prime. Of course, the notation Zn introduced in this section for the group of units of the ring Zn is consistent with the notation introduced in §2.5. Example 7.12. Every non-zero element of Q is a unit. Hence, Q is a field. Example 7.13. Every non-zero element of R is a unit. Hence, R is a field. Example 7.14. For non-zero ˛ D a C bi 2 C, with a; b 2 R, we have c WD N.˛/ D a2 C b 2 > 0. It follows that the complex number ˛c N 1 D .ac 1 / C 1 D 1. . bc 1 /i is the multiplicative inverse of ˛, since ˛ ˛c N 1 D .˛ ˛/c N Hence, every non-zero element of C is a unit, and so C is a field. Example 7.15. For rings R1 ; : : : ; Rk , it is easy to see that the multiplicative group of units of the direct product R1 Rk is equal to R1 Rk . Indeed, by definition, .a1 ; : : : ; ak / has a multiplicative inverse if and only if each individual ai does.
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Example 7.16. If I is an index set and R is a ring, then the units in Map.I; R/ are those functions f W I ! R such that f .i / 2 R for all i 2 I . Example 7.17. Consider the ring F of arithmetic functions defined in Example 7.6. By the result of Exercise 2.54, F D ff 2 F W f .1/ ¤ 0g. 7.1.2 Zero divisors and integral domains Let R be a ring. If a and b are non-zero elements of R such that ab D 0, then a and b are both called zero divisors. If R is non-trivial and has no zero divisors, then it is called an integral domain. Note that if a is a unit in R, it cannot be a zero divisor (if ab D 0, then multiplying both sides of this equation by a 1 yields b D 0). In particular, it follows that every field is an integral domain. Example 7.18. Z is an integral domain. Example 7.19. For n > 1, Zn is an integral domain if and only if n is prime. In particular, if n is composite, so n D ab with 1 < a < n and 1 < b < n, then Œan and Œbn are zero divisors: Œan Œbn D Œ0n , but Œan ¤ Œ0n and Œbn ¤ Œ0n . Example 7.20. Q, R, and C are fields, and hence are also integral domains. Example 7.21. For two non-trivial rings R1 ; R2 , an element .a1 ; a2 / 2 R1 R2 is a zero divisor if and only if a1 is a zero divisor, a2 is a zero divisor, or exactly one of a1 or a2 is zero. In particular, R1 R2 is not an integral domain. The next two theorems establish certain results that are analogous to familiar facts about integer divisiblity. These results hold in a general ring, provided one avoids zero divisors. The first is a cancellation law: Theorem 7.3. If R is a ring, and a; b; c 2 R such that a ¤ 0 and a is not a zero divisor, then ab D ac implies b D c. Proof. ab D bc implies a.b c/ D 0. The fact that a ¤ 0 and a is not a zero divisor implies that we must have b c D 0, and so b D c. Theorem 7.4. Let R be a ring. (i) Suppose a; b 2 R, and that either a or b is not a zero divisor. Then a j b and b j a if and only if ar D b for some r 2 R . (ii) Suppose a; b 2 R, a j b, a ¤ 0, and a is not b zero divisor. Then there exists a unique r 2 R such that ar D b, which we denote by b=a. Proof. For the first statement, if ar D b for some r 2 R , then we also have br 1 D a; thus, a j b and b j a. For the converse, suppose that a j b and b j a.
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We may assume that b is not a zero divisor (otherwise, exchange the roles of a and b). We may also assume that b is non-zero (otherwise, b j a implies a D 0, and so the conclusion holds with any r). Now, a j b implies ar D b for some r 2 R, and b j a implies br 0 D a for some r 0 2 R, and hence b D ar D br 0 r. Canceling b from both sides of the equation b D br 0 r, we obtain 1 D r 0 r, and so r is b unit. For the second statement, a j b means ar D b for some r 2 R. Moreover, this value of r is unique: if ar D b D ar 0 , then we may cancel a, obtaining r D r 0 . Of course, in the previous two theorems, if the ring is an integral domain, then there are no zero divisors, and so the hypotheses may be simplified in this case, dropping the explicit requirement that certain elements are not zero divisors. In particular, if a, b, and c are elements of an integral domain, such that ab D ac and a ¤ 0, then we can cancel a, obtaining b D c. The next two theorems state some facts pertaining specifically to integral domains. Theorem 7.5. The characteristic of an integral domain is either zero or a prime. Proof. By way of contradiction, suppose that D is an integral domain with characteristic m that is neither zero nor prime. Since, by definition, D is not a trivial ring, we cannot have m D 1, and so m must be composite. Say m D st , where 1 < s < m and 1 < t < m. Since m is the additive order of 1D , it follows that .s 1D / ¤ 0D and .t 1D / ¤ 0D ; moreover, since D is an integral domain, it follows that .s 1D /.t 1D / ¤ 0D . So we have 0D D m 1D D .st / 1D D .s 1D /.t 1D / ¤ 0D ; a contradiction. Theorem 7.6. Every finite integral domain is a field. Proof. Let D be a finite integral domain, and let a be any non-zero element of D. Consider the a-multiplication map that sends b 2 D to ab, which is a group homomorphism on the additive group of D. Since a is not a zero-divisor, it follows that the kernel of the a-multiplication map is f0D g, hence the map is injective, and by finiteness, it must be surjective as well. In particular, there must be an element b 2 D such that ab D 1D . Theorem 7.7. Every finite field F must be of cardinality p w , where p is prime, w is a positive integer, and p is the characteristic of F . Proof. By Theorem 7.5, the characteristic of F is either zero or a prime, and since F is finite, it must be prime. Let p denote the characteristic. By definition, p is the exponent of the additive group of F , and by Theorem 6.42, the primes dividing
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the exponent are the same as the primes dividing the order, and hence F must have cardinality p w for some positive integer w. Of course, for every prime p, Zp is a finite field of cardinality p. As we shall see later (in Chapter 19), for every prime p and positive integer w, there exists a field of cardinality p w . Later in this chapter, we shall see some specific examples of finite fields of cardinality p 2 (Examples 7.40, 7.59, and 7.60). E XERCISE 7.3. Let R be a ring, and let a; b 2 R such that ab ¤ 0. Show that ab is a zero divisor if and only if a is a zero divisor or b is a zero divisor. E XERCISE 7.4. Suppose that R is a non-trivial ring in which the cancellation law holds in general: for all a; b; c 2 R, if a ¤ 0 and ab D ac, then b D c. Show that R is an integral domain. E XERCISE 7.5. Let R be a ring of characteristic m > 0, and let n be an integer. Show that: (a) if gcd.n; m/ D 1, then n 1R is a unit; (b) if 1 < gcd.n; m/ < m, then n 1R is a zero divisor; (c) otherwise, n 1R D 0. E XERCISE 7.6. Let D be an integral domain, m 2 Z, and a 2 D. Show that ma D 0 if and only if m is a multiple of the characteristic of D or a D 0. E XERCISE 7.7. Show that for all n 1, and for all a; b 2 Zn , if a j b and b j a, then ar D b for some r 2 Zn . Hint: this result does not follow from part (i) of Theorem 7.4, as we allow a and b to be zero divisors here; first consider the case where n is a prime power. E XERCISE 7.8. Show that the ring F of arithmetic functions defined in Example 7.6 is an integral domain. E XERCISE 7.9. This exercise depends on results in §6.6. Using the fundamental theorem of finite abelian groups, show that the additive group of a finite field of characteristic p and cardinality p w is isomorphic to Zpw . 7.1.3 Subrings Definition 7.8. A subset S of a ring R is called a subring if (i) S is an additive subgroup of R, (ii) S is closed under multiplication, and (iii) 1R 2 S .
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It is clear that the operations of addition and multiplication on a ring R make a subring S of R into a ring, where 0R is the additive identity of S and 1R is the multiplicative identity of S. One may also call R an extension ring of S . Some texts do not require that 1R belongs to a subring S , and instead require only that S contains a multiplicative identity, which may be different than that of R. This is perfectly reasonable, but for simplicity, we restrict ourselves to the case where 1R 2 S. Expanding the above definition, we see that a subset S of R is a subring if and only if 1R 2 S and for all a; b 2 S, we have a C b 2 S;
a 2 S; and ab 2 S:
In fact, to verify that S is a subring, it suffices to show that 1R 2 S and that S is closed under addition and multiplication; indeed, if 1R 2 S and S is closed under multiplication, then S is closed under negation, and further, 1R D . 1R / 2 S . Example 7.22. Z is a subring of Q. Example 7.23. Q is a subring of R. Example 7.24. R is a subring of C. Note that for all ˛ WD a C bi 2 C, with a; b 2 R, we have ˛N D ˛ ” a C bi D a bi ” b D 0. That is, ˛N D ˛ ” ˛ 2 R. Example 7.25. The set ZŒi of complex numbers of the form aCbi , with a; b 2 Z, is a subring of C. It is called the ring of Gaussian integers. Since C is a field, C contains no zero divisors, and hence ZŒi contains no zero divisors either. Hence, ZŒi is an integral domain. Let us determine the units of ZŒi . If ˛ 2 ZŒi is a unit, then there exists 0 ˛ 2 ZŒi such that ˛˛ 0 D 1. Taking norms, we obtain 1 D N.1/ D N.˛˛ 0 / D N.˛/N.˛ 0 /: Clearly, the norm of a Gaussian integer is a non-negative integer, and so N.˛/N.˛ 0 / D 1 implies N.˛/ D 1. Now, if ˛ D a C bi, with a; b 2 Z, then N.˛/ D a2 C b 2 , and so N.˛/ D 1 implies ˛ D ˙1 or ˛ D ˙i . Conversely, it is clear that ˙1 and ˙i are indeed units, and so these are the only units in ZŒi . Example 7.26. Let m be a positive integer, and let Q.m/ be the set of rational numbers which can be written as a=b, where a and b are integers, and b is relatively prime to m. Then Q.m/ is a subring of Q, since for all a; b; c; d 2 Z with gcd.b; m/ D 1 and gcd.d; m/ D 1, we have a c ad C bc a c ac C D and D ; b d bd b d bd
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and since gcd.bd; m/ D 1, it follows that the sum and product of any two elements of Q.m/ is again in Q.m/ . Clearly, Q.m/ contains 1, and so it follows that Q.m/ is a subring of Q. The units of Q.m/ are precisely those rational numbers of the form a=b, where gcd.a; m/ D gcd.b; m/ D 1. Example 7.27. Suppose R is a non-trivial ring. Then the set f0R g is not a subring of R: although it satisfies the first two requirements of the definition of a subring, it does not satisfy the third. Generalizing the argument in Example 7.25, it is clear that every subring of an integral domain is itself an integral domain. However, it is not the case that a subring of a field is always a field: the subring Z of Q is a counter-example. If F 0 is a subring of a field F , and F 0 is itself a field, then we say that F 0 is a subfield of F , and that F is an extension field of F 0 . For example, Q is a subfield of R, which in turn is a subfield of C. E XERCISE 7.10. Show that if S and T are subrings of R, then so is S \ T . E XERCISE 7.11. Let S1 be a subring of R1 , and S2 a subring of R2 . Show that S1 S2 is a subring of R1 R2 . E XERCISE 7.12. Suppose that S and T are subrings of a ring R. Show that their ring-theoretic product S T (see Exercise 7.2) is a subring of R that contains S [ T , and is the smallest such subring. E XERCISE 7.13. Show that the set QŒi of complex numbers of the form a C bi, with a; b 2 Q, is a subfield of C. E XERCISE 7.14. Consider the ring Map.R; R/ of functions f W R ! R, with addition and multiplication defined point-wise. (a) Show that Map.R; R/ is not an integral domain, and that Map.R; R/ consists of those functions that never vanish. (b) Let a; b 2 Map.R; R/. Show that if a j b and b j a, then ar D b for some r 2 Map.R; R/ . (c) Let C be the subset of Map.R; R/ of continuous functions. Show that C is a subring of Map.R; R/, and that all functions in C are either everywhere positive or everywhere negative. (d) Find elements a; b 2 C, such that in the ring C, we have a j b and b j a, yet there is no r 2 C such that ar D b.
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7.2 Polynomial rings If R is a ring, then we can form the ring of polynomials RŒX, consisting of all polynomials g D a0 C a1 X C C ak Xk in the indeterminate, or “formal” variable, X, with coefficients ai in R, and with addition and multiplication defined in the usual way. Example 7.28. Let us define a few polynomials over the ring Z: a WD 3 C X2 ; b WD 1 C 2X
X3 ; c WD 5; d WD 1 C X; e WD X; f WD 4X3 :
We have aCb D 4C2XCX2 X3 ; ab D 3C6XCX2 X3 X5 ; cd Cef D 5C5XC4X4 : As illustrated in the previous example, elements of R are also polynomials. Such polynomials are called constant polynomials; all other polynomials are called non-constant polynomials. The set R of constant polynomials forms a subring of RŒX. In particular, 0R is the additive identity in RŒX and 1R is the multiplicative identity in RŒX. Note that if R is the trivial ring, then so is RŒX; also, if R is a subring of E, then RŒX is a subring of EŒX. So as to keep the distinction between ring elements and indeterminates clear, we shall use the symbol “X” only to denote the latter. Also, for a polynomial g 2 RŒX, we shall in general write this simply as “g,” and not as “g.X/.” Of course, the choice of the symbol “X” is arbitrary; occasionally, we may use another symbol, such as “Y,” as an alternative. 7.2.1 Formalities For completeness, we present a more formal definition of the ring RŒX. The reader should bear in mind that this formalism is rather tedious, and may be more distracting than it is enlightening. Formally, a polynomial g 2 RŒX is an infinite sequence fai g1 i D0 , where each ai 2 R, but only finitely many of the ai ’s are non-zero. Intuitively, ai represents the coefficient of Xi . For non-negative integer j and c 2 R, it will be convenient to define j .c/ to be the sequence fci g1 i D0 2 RŒX, where cj WD c and ci WD 0R for i ¤ j . For 1 g D fai g1 i D0 2 RŒX and h D fbi gi D0 2 RŒX;
we define 1 g C h WD fsi g1 i D0 and gh WD fpi gi D0 ;
where for i D 0; 1; 2; : : : ; si WD ai C bi
(7.2)
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and pi WD
X
aj bk ;
(7.3)
i Dj Ck
the sum being over all pairs .j; k/ of non-negative integers such that i D j C k (which is a finite sum). We leave it to the reader to verify that g C h and gh are polynomials (i.e., only finitely many of the si ’s and pi ’s are non-zero). The reader may also verify that all the requirements of Definition 7.1 are satisfied: the additive identity is the all-zero sequence, and the multiplicative identity is 0 .1R /. One can easily verify that for all c; d 2 R, we have 0 .c C d / D 0 .c/ C 0 .d / and 0 .cd / D 0 .c/0 .d /: We shall identify c 2 R with 0 .c/ 2 RŒX, viewing the ring element c as simply “shorthand” for the polynomial 0 .c/ in contexts where a polynomial is expected. Note that while c and 0 .c/ are not the same mathematical object, there will certainly be no possible confusion in treating them as such. Thus, from a narrow, legalistic point of view, R is not a subring of RŒX, but we shall not let such annoying details prevent us from continuing to speak of it as such. Indeed, by appropriately renaming elements, we can make R a subring of RŒX in the literal sense of the term. We also define X WD 1 .1R /. One can easily verify that Xi D i .1R / for all i 0. More generally, for any polynomial g D fai g1 i D0 , if ai D 0R for all i Pk exceeding some value k, then we have g D i D0 0 .ai /Xi . Writing ai in place of P 0 .ai /, we have g D kiD0 ai Xi , and so we can return to the standard practice of writing polynomials as we did in Example 7.28, without any loss of precision. 7.2.2 Basic properties of polynomial rings P Let R be a ring. For non-zero g 2 RŒX, if g D kiD0 ai Xi with ak ¤ 0, then we call k the degree of g, denoted deg.g/, we call ak the leading coefficient of g, denoted lc.g/, and we call a0 the constant term of g. If lc.g/ D 1, then g is called monic. P P Suppose g D kiD0 ai Xi and h D `iD0 bi Xi are polynomials such that ak ¤ 0 and b` ¤ 0, so that deg.g/ D k and lc.g/ D ak , and deg.h/ D ` and lc.h/ D b` . When we multiply these two polynomials, we get gh D a0 b0 C .a0 b1 C a1 b0 /X C C ak b` XkC` : In particular, deg.gh/ deg.g/Cdeg.h/. If either of ak or b` are not zero divisors, then ak b` is not zero, and hence deg.gh/ D deg.g/ C deg.h/. However, if both ak
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and b` are zero divisors, then we may have ak b` D 0, in which case, the product gh may be zero, or perhaps gh ¤ 0 but deg.gh/ < deg.g/ C deg.h/. For the zero polynomial, we establish the following conventions: its leading coefficient and constant term are defined to be 0R , and its degree is defined to be 1. With these conventions, we may succinctly state that for all g; h 2 RŒX, we have deg.gh/ deg.g/ C deg.h/, with equality guaranteed to hold unless the leading coefficients of both g and h are zero divisors. In particular, if the leading coefficient of a polynomial is not a zero divisor, then the polynomial is not a zero divisor. In the case where the ring of coefficients is an integral domain, we can be more precise: Theorem 7.9. Let D be an integral domain. Then: (i) for all g; h 2 DŒX, we have deg.gh/ D deg.g/ C deg.h/; (ii) DŒX is an integral domain; (iii) .DŒX/ D D . Proof. Exercise. An extremely important property of polynomials is a division with remainder property, analogous to that for the integers: Theorem 7.10 (Division with remainder property). Let R be a ring. For all g; h 2 RŒX with h ¤ 0 and lc.h/ 2 R , there exist unique q; r 2 RŒX such that g D hq C r and deg.r/ < deg.h/. Proof. Consider the set S WD fg ht W t 2 RŒXg. Let r D g hq be an element of S of minimum degree. We must have deg.r/ < deg.h/, since otherwise, we could subtract an appropriate multiple of h from r so as to eliminate the leading coefficient of r, obtaining r 0 WD r
h .lc.r/ lc.h/
1 deg.r/ deg.h/
X
/ 2 S;
where deg.r 0 / < deg.r/, contradicting the minimality of deg.r/. That proves the existence of r and q. For uniqueness, suppose that g D hq C r and g D hq 0 C r 0 , where deg.r/ < deg.h/ and deg.r 0 / < deg.h/. This implies r 0 r D h .q q 0 /: However, if q ¤ q 0 , then deg.h/ > deg.r 0
r/ D deg.h .q
q 0 // D deg.h/ C deg.q
q 0 / deg.h/;
which is impossible. Therefore, we must have q D q 0 , and hence r D r 0 . If g D hq Cr as in the above theorem, we define g mod h WD r. Clearly, h j g if
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and only if g mod h D 0. Moreover, note that if deg.g/ < deg.h/, then q D 0 and r D g; otherwise, if deg.g/ deg.h/, then q ¤ 0 and deg.g/ D deg.h/ C deg.q/. 7.2.3 Polynomial evaluation P Of course, a polynomial g D kiD0 ai Xi defines a polynomial function on R that P sends x 2 R to kiD0 ai x i 2 R, and we denote the value of this function as g.x/ (note that “X” denotes an indeterminate, while “x” denotes an element of R). It is important to regard polynomials over R as formal expressions, and not to identify them with their corresponding functions. In particular, two polynomials are equal if and only if their coefficients are equal, while two functions are equal if and only if their values agree at all points in R. This distinction is important, since there are rings R over which two different polynomials define the same function. One can of course define the ring of polynomial functions on R, but in general, that ring has a different structure from the ring of polynomials over R. Example 7.29. In the ring Zp , for prime p, by Fermat’s little theorem (Theorem 2.14), we have x p D x for all x 2 Zp . However, the polynomials Xp and X are not the same polynomials (in particular, the former has degree p, while the latter has degree 1). P More generally, if R is a subring of a ring E, a polynomial g D kiD0 ai Xi 2 P RŒX defines a polynomial function from E to E that sends ˛ 2 E to kiD0 ai ˛ i 2 E, and the value of this function is denoted g.˛/. We say that ˛ is a root of g if g.˛/ D 0. An obvious, yet important, fact is the following: Theorem 7.11. Let R be a subring of a ring E. For all g; h 2 RŒX and ˛ 2 E, if s WD g C h 2 RŒX and p WD gh 2 RŒX, then we have s.˛/ D g.˛/ C h.˛/ and p.˛/ D g.˛/h.˛/: Also, if c 2 R is a constant polynomial, then c.˛/ D c for all ˛ 2 E. Proof. The statement about evaluating a constant polynomial is clear from the definitions. The proof of the statements about evaluating the sum or product of P i polynomials is really just symbol pushing. Indeed, suppose g D i ai X and P P h D i bi Xi . Then s D i .ai C bi /Xi , and so X X X s.˛/ D .ai C bi /˛ i D ai ˛ i C bi ˛ i D g.˛/ C h.˛/: i
i
i
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Also, we have pD
X
ai X
i
X
i
bj X
j
D
j
X
ai bj Xi Cj ;
i;j
and employing the result for evaluating sums of polynomials, we have X X X i Cj i j D D g.˛/h.˛/: p.˛/ D ai bj ˛ ai ˛ bj ˛ i;j
i
j
Example 7.30. Consider the polynomial g WD 2X3 2X2 C X 1 2 ZŒX. We can write g D .2X2 C 1/.X 1/. For any element ˛ of Z, or an extension ring of Z, we have g.˛/ D .2˛ 2 C 1/.˛ 1/. From this, it is clear that in Z, g has aproot only at 1; moreover, it has no other roots in R, but in C, it also has roots ˙i= 2. Example 7.31. If E D RŒX, then evaluating a polynomial g 2 RŒX at a point ˛ 2 E amounts to polynomial composition. For example, if g WD X2 C X then g X C 1 D .X C 1/2 C .X C 1/ D X2 C 3X C 2: The reader is perhaps familiar with the fact that over the real or the complex numbers, every polynomial of degree k has at most k distinct roots, and the fact that every set of k points can be interpolated by a unique polynomial of degree less than k. As we will now see, these results extend to much more general, though not completely arbitrary, coefficient rings. Theorem 7.12. Let R be a ring, g 2 RŒX, and x 2 R. Then there exists a unique polynomial q 2 RŒX such that g D .X x/q C g.x/. In particular, x is a root of g if and only if .X x/ divides g. Proof. If R is the trivial ring, there is nothing to prove, so assume that R is nontrivial. Using the division with remainder property for polynomials, there exist unique q; r 2 RŒX such that g D .X x/q C r, with q; r 2 RŒX and deg.r/ < 1, which means that r 2 R. Evaluating at x, we see that g.x/ D .x x/q.x/Cr D r. That proves the first statement. The second follows immediately from the first. Note that the above theorem says that X x divides g g.x/, and the polynomial q in the theorem may be expressed as qD
g
g.x/ : X x
Theorem 7.13. Let D be an integral domain, and let x1 ; : : : ; xk be distinct elements of D. Then for every polynomial g 2 DŒX, the elements x1 ; : : : ; xk are Q roots of g if and only if the polynomial kiD1 .X xi / divides g.
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Qk
Proof. One direction is trivial: if i D1 .X xi / divides g, then it is clear that each xi is a root of g. We prove the converse by induction on k. The base case k D 1 is just Theorem 7.12. So assume k > 1, and that the statement holds for k 1. Let g 2 DŒX and let x1 ; : : : ; xk be distinct roots of g. Since xk is a root of g, then by Theorem 7.12, there exists q 2 DŒX such that g D .X xk /q. Moreover, for each i D 1; : : : ; k 1, we have 0 D g.xi / D .xi
xk /q.xi /;
and since xi xk ¤ 0 and D is an integral domain, we must have q.xi / D 0. Q Thus, q has roots x1 ; : : : ; xk 1 , and by induction ki D11 .X xi / divides q, from Q which it then follows that kiD1 .X xi / divides g. Note that in this theorem, we can slightly weaken the hypothesis: we do not need to assume that the coefficient ring is an integral domain; rather, all we really need is that for all i ¤ j , the difference xi xj is not a zero divisor. As an immediate consequence of this theorem, we obtain: Theorem 7.14. Let D be an integral domain, and let g 2 DŒX, with deg.g/ D k 0. Then g has at most k distinct roots. Proof. If g had k C 1 distinct roots x1 ; : : : ; xkC1 , then by the previous theorem, Q the polynomial kC1 i D1 .X xi /, which has degree k C 1, would divide g, which has degree k —an impossibility. Theorem 7.15 (Lagrange interpolation). Let F be a field, let x1 ; : : : ; xk be distinct elements of F , and let y1 ; : : : ; yk be arbitrary elements of F . Then there exists a unique polynomial g 2 F ŒX with deg.g/ < k such that g.xi / D yi for i D 1; : : : ; k, namely Q k X xj / j ¤i .X : g WD yi Q xj / j ¤i .xi i D1
Proof. For the existence part of the theorem, one just has to verify that g.xi / D yi for the given g, which clearly has degree less than k. This is easy to see: for i D 1; : : : ; k, evaluating the i th term in the sum defining g at xi yields yi , while evaluating any other term at xi yields 0. The uniqueness part of the theorem follows almost immediately from Theorem 7.14: if g and h are polynomials of degree less then k such that g.xi / D yi D h.xi / for i D 1; : : : ; k, then g h is a polynomial of degree less than k with k distinct roots, which, by the previous theorem, is impossible. Again, we can slightly weaken the hypothesis of this theorem: we do not need
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to assume that the coefficient ring is a field; rather, all we really need is that for all i ¤ j , the difference xi xj is a unit. E XERCISE 7.15. Let D be an infinite integral domain, and let g; h 2 DŒX. Show that if g.x/ D h.x/ for all x 2 D, then g D h. Thus, for an infinite integral domain D, there is a one-to-one correspondence between polynomials over D and polynomial functions on D. E XERCISE 7.16. Let F be a field. (a) Show that for all b 2 F , we have b 2 D 1 if and only if b D ˙1. (b) Show that for all a; b 2 F , we have a2 D b 2 if and only if a D ˙b. (c) Show that the familiar quadratic formula holds for F , assuming F has characteristic other than 2, so that 2F ¤ 0F . That is, for all a; b; c 2 F with a ¤ 0, the polynomial g WD aX2 C bX C c 2 F ŒX has a root in F if and only if there exists e 2 F such that e 2 D d , where d is the discriminant of g, defined as d WD b 2 4ac, and in this case the roots of g are . b ˙ e/=2a. E XERCISE 7.17. Let R be a ring, let g 2 RŒX, with deg.g/ D k 0, and let x be an element of R. Show that: (a) there exist an integer m, with 0 m k, and a polynomial q 2 RŒX, such that g D .X
x/m q and q.x/ ¤ 0;
and moreover, the values of m and q are uniquely determined; (b) if we evaluate g at X C x, we have k X g XCx D bi Xi ; i D0
where b0 D D bm
1
D 0 and bm D q.x/ ¤ 0.
Let mx .g/ denote the value m in the previous exercise; for completeness, one can define mx .g/ WD 1 if g is the zero polynomial. If mx .g/ > 0, then x is called a root of g of multiplicity mx .g/; if mx .g/ D 1, then x is called a simple root of g, and if mx .g/ > 1, then x is called a multiple root of g. The following exercise refines Theorem 7.14, taking into account multiplicities. E XERCISE 7.18. Let D be an integral domain, and let g 2 DŒX, with deg.g/ D k 0. Show that X mx .g/ k: x2D
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E XERCISE 7.19. Let D be an integral domain, let g; h 2 DŒX, and let x 2 D. Show that mx .gh/ D mx .g/ C mx .h/. 7.2.4 Multi-variate polynomials One can naturally generalize the notion of a polynomial in a single variable to that of a polynomial in several variables. Consider the ring RŒX of polynomials over a ring R. If Y is another indeterminate, we can form the ring RŒXŒY of polynomials in Y whose coefficients are themselves polynomials in X over the ring R. One may write RŒX; Y instead of RŒXŒY. An element of RŒX; Y is called a bivariate polynomial. Consider a typical element g 2 RŒX; Y, which may be written ` X k X i gD aij X Yj : (7.4) j D0 i D0
Rearranging terms, this may also be written as X gD aij Xi Yj ;
(7.5)
0i k 0j `
or as gD
k X ` X
aij Y
j
Xj :
(7.6)
i D0 j D0
If g is written as in (7.5), the terms Xi Yj are called monomials. The total degree of such a monomial Xi Yj is defined to be i C j , and if g is non-zero, then the total degree of g, denoted Deg.g/, is defined to be the maximum total degree among all monomials Xi Yj appearing in (7.5) with a non-zero coefficient aij . We define the total degree of the zero polynomial to be 1. When g is written as in (7.6), one sees that we can naturally view g as an element of RŒYŒX, that is, as a polynomial in X whose coefficients are polynomials in Y. From a strict, syntactic point of view, the rings RŒYŒX and RŒXŒY are not the same, but there is no harm done in blurring this distinction when convenient. We denote by degX .g/ the degree of g, viewed as a polynomial in X, and by degY .g/ the degree of g, viewed as a polynomial in Y. Example 7.32. Let us illustrate, with a particular example, the three different forms — as in (7.4), (7.5), and (7.6) — of expressing a bivariate polynomial. In
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the ring ZŒX; Y we have g D .5X2
3X C 4/Y C .2X2 C 1/
D 5X2 Y C 2X2
3XY C 4Y C 1
D .5Y C 2/X2 C . 3Y/X C .4Y C 1/: We have Deg.g/ D 3, degX .g/ D 2, and degY .g/ D 1. More generally, if X1 ; : : : ; Xn are indeterminates, we can form the ring RŒX1 ; : : : ; Xn of multi-variate polynomials in n variables over R. Formally, we can define this ring recursively as RŒX1 ; : : : ; Xn 1 ŒXn , that is, the ring of polynomials in the variable Xn , with coefficients in RŒX1 ; : : : ; Xn 1 . A monomial is a term of the form Xe11 Xenn , and the total degree of such a monomial is e1 C C en . Every non-zero multi-variate polynomial g can be expressed uniquely (up to a re-ordering of terms) as a1 1 C C ak k , where each ai is a non-zero element of R, and each i is a monomial; we define the total degree of g, denoted Deg.g/, to be the maximum of the total degrees of the i ’s. As usual, the zero polynomial is defined to have total degree 1. Just as for bivariate polynomials, the order of the indeterminates is not important, and for every i D 1; : : : ; n, one can naturally view any g 2 RŒX1 ; : : : ; Xn as a polynomial in Xi over the ring RŒX1 ; : : : ; Xi 1 ; Xi C1 ; : : : ; Xn , and define degXi .g/ to be the degree of g when viewed in this way. Just as polynomials in a single variable define polynomial functions, so do polynomials in several variables. If R is a subring of E, g 2 RŒX1 ; : : : ; Xn , and ˛1 ; : : : ; ˛n 2 E, we define g.˛1 ; : : : ; ˛n / to be the element of E obtained by evaluating the expression obtained by substituting ˛i for Xi in g. Theorem 7.11 carries over directly to the multi-variate case. E XERCISE 7.20. Let R be a ring, and consider the ring of multi-variate polynomials RŒX1 ; : : : ; Xn . For m 0, define Hm to be the subset of polynomials that can be expressed as a1 1 C C ak k , where each ai belongs to R and each i is a monomial of total degree m (by definition, Hm includes the zero polynomial, and H0 D R). Polynomials that belong to Hm for some m are called homogeneous polynomials. Show that: (a) if g; h 2 Hm , then g C h 2 Hm ; (b) if g 2 H` and h 2 Hm , then gh 2 H`Cm ; (c) every non-zero polynomial g can be expressed uniquely as g0 C C gd , where gi 2 Hi for i D 0; : : : ; d , gd ¤ 0, and d D Deg.g/; (d) for all polynomials g; h, we have Deg.gh/ Deg.g/ C Deg.h/, and if R is an integral domain, then Deg.gh/ D Deg.g/ C Deg.h/.
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E XERCISE 7.21. Suppose that D is an integral domain, and g; h are non-zero, multi-variate polynomials over D such that gh is homogeneous. Show that g and h are also homogeneous. E XERCISE 7.22. Let R be a ring, and let x1 ; : : : ; xn be elements of R. Show that every polynomial g 2 RŒX1 ; : : : ; Xn can be expressed as g D .X1
x1 /q1 C C .Xn
xn /qn C g.x1 ; : : : ; xn /;
where q1 ; : : : ; qn 2 RŒX1 ; : : : ; Xn . E XERCISE 7.23. This exercise generalizes Theorem 7.14. Let D be an integral domain, and let g 2 DŒX1 ; : : : ; Xn , with Deg.g/ D k 0. Let T be a finite subset of D. Show that the number of elements .x1 ; : : : ; xn / 2 T n such that g.x1 ; : : : ; xn / D 0 is at most kjT jn 1 . 7.3 Ideals and quotient rings Definition 7.16. Let R be a ring. An ideal of R is an additive subgroup I of R that is closed under multiplication by elements of R, that is, for all a 2 I and r 2 R, we have ar 2 I . Expanding the above definition, we see that a non-empty subset I of R is an ideal of R if and only if for all a; b 2 I and r 2 R, we have a C b 2 I;
a 2 I; and ar 2 I:
Since R is commutative, the condition ar 2 I is equivalent to ra 2 I . The condition a 2 I is redundant, as it is implied by the condition ar 2 I with r D 1R . In the case when R is the ring Z, this definition of an ideal is consistent with that given in §1.2. Clearly, f0R g and R are ideals of R. From the fact that an ideal I is closed under multiplication by elements of R, it is easy to see that I D R if and only if 1R 2 I . Example 7.33. For each m 2 Z, the set mZ is not only an additive subgroup of the ring Z, it is also an ideal of this ring. Example 7.34. For each m 2 Z, the set mZn is not only an additive subgroup of the ring Zn , it is also an ideal of this ring. Example 7.35. In the previous two examples, we saw that for some rings, the notion of an additive subgroup coincides with that of an ideal. Of course, that is the exception, not the rule. Consider the ring of polynomials RŒX. Suppose g is a non-zero polynomial in RŒX. The additive subgroup generated by g contains only polynomials whose degrees are at most that of g. However, this subgroup is not an
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ideal, since every ideal containing g must also contain g Xi for all i 0, and must therefore contain polynomials of arbitrarily high degree. Example 7.36. Let R be a ring and x 2 R. Consider the set I WD fg 2 RŒX W g.x/ D 0g. It is not hard to see that I is an ideal of RŒX. Indeed, for all g; h 2 I and q 2 RŒX, we have .g C h/.x/ D g.x/ C h.x/ D 0 C 0 D 0 and .gq/.x/ D g.x/q.x/ D 0 q.x/ D 0. Moreover, by Theorem 7.12, we have I D f.X x/q W q 2 RŒXg. We next develop some general constructions of ideals. Theorem 7.17. Let R be a ring and let a 2 R. Then aR WD far W r 2 Rg is an ideal of R. Proof. This is an easy calculation. For all ar; ar 0 2 aR and r 00 2 R, we have ar C ar 0 D a.r C r 0 / 2 aR and .ar/r 00 D a.rr 00 / 2 aR. The ideal aR in the previous theorem is called the ideal of R generated by a. Since R is commutative, one could also write this ideal as Ra WD fra W r 2 Rg. It is easy to see that this ideal contains a, and is the smallest ideal of R with this property. An ideal of this form is called a principal ideal. Corresponding to Theorems 6.11 and 6.12, we have: Theorem 7.18. If I1 and I2 are ideals of a ring R, then so are I1 C I2 and I1 \ I2 . Proof. We already know that I1 C I2 and I1 \ I2 are additive subgroups of R, so it suffices to show that they are closed under multiplication by elements of R. The reader may easily verify that this is the case. Let a1 ; : : : ; ak be elements of a ring R. The ideal a1 R C C ak R is called the ideal of R generated by a1 ; : : : ; ak . When the ring R is clear from context, one often writes .a1 ; : : : ; ak / to denote this ideal. It is easy to see that this ideal contains a1 ; : : : ; ak , and is the smallest ideal of R with this property. Example 7.37. Let n be a positive integer, and let x be any integer. Define I WD fg 2 ZŒX W g.x/ 0 .mod n/g. We claim that I is the ideal .X x; n/ of ZŒX. To see this, consider any fixed g 2 ZŒX. Using Theorem 7.12, we have g D .X x/q C g.x/ for some q 2 ZŒX. Using the division with remainder property for integers, we have g.x/ D nq 0 C r for some r 2 f0; : : : ; n 1g and q 0 2 Z. Thus, g.x/ r .mod n/, and if g.x/ 0 .mod n/, then we must have r D 0, and hence g D .X x/q C nq 0 2 .X x; n/. Conversely, if g 2 .X x; n/, we can write g D .X x/q C nq 0 for some q; q 0 2 ZŒX, and from this, it is clear that g.x/ D nq 0 .x/ 0 .mod n/. Let I be an ideal of a ring R. Since I is an additive subgroup of R, we may adopt
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the congruence notation in §6.3, writing a b .mod I / to mean a b 2 I , and we can form the additive quotient group R=I of cosets. Recall that for a 2 R, the coset of R containing a is denoted ŒaI , and that ŒaI D a C I D fa C x W x 2 I g. Also recall that addition in R=I was defined in terms of addition of coset representatives; that is, for a; b 2 I , we defined ŒaI C ŒbI WD Œa C bI : Theorem 6.16 ensured that this definition was unambiguous. Our goal now is to make R=I into a ring by similarly defining multiplication in R=I in terms of multiplication of coset representatives. To do this, we need the following multiplicative analog of Theorem 6.16, which exploits in an essential way the fact that an ideal is closed under multiplication by elements of R; in fact, this is one of the main motivations for defining the notion of an ideal as we did. Theorem 7.19. Let I be an ideal of a ring R, and let a; a0 ; b; b 0 2 R. If a a0 .mod I / and b b 0 .mod I /, then ab a0 b 0 .mod I /. Proof. If a D a0 C x for some x 2 I and b D b 0 C y for some y 2 I , then ab D a0 b 0 Ca0 y Cb 0 x Cxy. Since I is closed under multiplication by elements of R, we see that a0 y; b 0 x; xy 2 I , and since I is closed under addition, a0 y Cb 0 x Cxy 2 I . Hence, ab a0 b 0 2 I . Using this theorem we can now unambiguously define multiplication on R=I as follows: for a; b 2 R, ŒaI ŒbI WD ŒabI : Once that is done, it is straightforward to verify that all the properties that make R a ring are inherited by R=I — we leave the details of this to the reader. In particular, the multiplicative identity of R=I is the coset Œ1R I . The ring R=I is called the quotient ring or residue class ring of R modulo I . Elements of R=I may be called residue classes. Note that if I D dR, then a b .mod I / if and only if d j .a b/, and as a matter of notation, one may simply write this congruence as a b .mod d /. We may also write Œad instead of ŒaI . Finally, note that if I D R, then R=I is the trivial ring. Example 7.38. For each n 1, the ring Zn is precisely the quotient ring Z=nZ. Example 7.39. Let f be a polynomial over a ring R with deg.f / D ` 0 and lc.f / 2 R , and consider the quotient ring E WD RŒX=f RŒX. By the division with remainder property for polynomials (Theorem 7.10), for every g 2 RŒX, there
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187
exists a unique polynomial h 2 RŒX such that g h .mod f / and deg.h/ < `. From this, it follows that every element of E can be written uniquely as Œhf , where h 2 RŒX is a polynomial of degree less than `. Note that in this situation, we will generally prefer the more compact notation RŒX=.f /, instead of RŒX=f RŒX. Example 7.40. Consider the polynomial f WD X2 CXC1 2 Z2 ŒX and the quotient ring E WD Z2 ŒX=.f /. Let us name the elements of E as follows: 00 WD Œ0f ; 01 WD Œ1f ; 10 WD ŒXf ; 11 WD ŒX C 1f : With this naming convention, addition of two elements in E corresponds to just computing the bit-wise exclusive-or of their names. More precisely, the addition table for E is the following: C 00 01 10 11
00 00 01 10 11
01 01 00 11 10
10 10 11 00 01
11 11 10 01 00
Note that 00 acts as the additive identity for E, and that as an additive group, E is isomorphic to the additive group Z2 Z2 . As for multiplication in E, one has to compute the product of two polynomials, and then reduce modulo f . For example, to compute 10 11, using the identity X2 X C 1 .mod f /, one sees that X .X C 1/ X2 C X .X C 1/ C X 1 .mod f /I thus, 10 11 D 01. The reader may verify the following multiplication table for E: 00 01 10 11
00 00 00 00 00
01 00 01 10 11
10 00 10 11 01
11 00 11 01 10
Observe that 01 acts as the multiplicative identity for E. Notice that every nonzero element of E has a multiplicative inverse, and so E is in fact a field. Observe that E is cyclic: the reader may verify that both 10 and 11 have multiplicative order 3. This is the first example we have seen of a finite field whose cardinality is not prime. E XERCISE 7.24. Show that if F is a field, then the only ideals of F are f0F g and F.
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E XERCISE 7.25. Let a; b be elements of a ring R. Show that a j b ” b 2 aR ” bR aR: E XERCISE 7.26. Let R be a ring. Show that if I is a non-empty subset of RŒX that is closed under addition, multiplication by elements of R, and multiplication by X, then I is an ideal of RŒX. E XERCISE 7.27. Let I be an ideal of R, and S a subring of R. Show that I \ S is an ideal of S . E XERCISE 7.28. Let I be an ideal of R, and S a subring of R. Show that I C S is a subring of R, and that I is an ideal of I C S . E XERCISE 7.29. Let I1 be an ideal of R1 , and I2 an ideal of R2 . Show that I1 I2 is an ideal of R1 R2 . E XERCISE 7.30. Write down the multiplication table for Z2 ŒX=.X2 C X/. Is this a field? E XERCISE 7.31. Let I be an ideal of a ring R, and let x and y be elements of R with x y .mod I /. Let g 2 RŒX. Show that g.x/ g.y/ .mod I /. E XERCISE 7.32. Let R be a ring, and fix x1 ; : : : ; xn 2 R. Let I WD fg 2 RŒX1 ; : : : ; Xn W g.x1 ; : : : ; xn / D 0g. Show that I is an ideal of RŒX1 ; : : : ; Xn , and that I D .X1 x1 ; : : : ; Xn xn /. E XERCISE 7.33. Let p be a prime, and consider the ring Q.p/ (see Example 7.26). Show that every non-zero ideal of Q.p/ is of the form .p i /, for some uniquely determined integer i 0. E XERCISE 7.34. Let p be a prime. Show that in the ring ZŒX, the ideal .X; p/ is not a principal ideal. E XERCISE 7.35. Let F be a field. Show that in the ring F ŒX; Y, the ideal .X; Y/ is not a principal ideal. E XERCISE 7.36. Let R be a ring, and let fIi g1 of ideals of R i D1 be a sequence S such that Ii Ii C1 for all i D 1; 2; 3; : : : : Show that the union 1 i D1 Ii is also an ideal of R. An ideal I of a ring R is called prime if I ¨ R and if for all a; b 2 R, ab 2 I implies a 2 I or b 2 I . An ideal I of a ring R is called maximal if I ¨ R and there are no ideals J of R such that I ¨ J ¨ R. E XERCISE 7.37. Let R be a ring. Show that:
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189
(a) an ideal I of R is prime if and only if R=I is an integral domain; (b) an ideal I of R is maximal if and only if R=I is a field; (c) all maximal ideals of R are also prime ideals. E XERCISE 7.38. This exercise explores some examples of prime and maximal ideals. Show that: (a) in the ring Z, the ideal f0g is prime but not maximal, and that the maximal ideals are precisely those of the form pZ; where p is prime. (b) in an integral domain D, the ideal f0g is prime, and this ideal is maximal if and only if D is a field; (c) if p is a prime, then in the ring ZŒX, the ideal .X; p/ is maximal, while the ideals .X/ and .p/ are prime, but not maximal; (d) if F is a field, then in the ring F ŒX; Y, the ideal .X; Y/ is maximal, while the ideals .X/ and .Y/ are prime, but not maximal. E XERCISE 7.39. It is a fact that every non-trivial ring R contain at least one maximal ideal. Showing this in general requires some fancy set-theoretic notions. This exercise develops a simple proof in the case where R is countable (see §A3). (a) Show that if R is non-trivial but finite, then it contains a maximal ideal. (b) Assume that R is countably infinite, and let a1 ; a2 ; a3 ; : : : be an enumeration of the elements of R. Define a sequence of ideals I0 ; I1 ; I2 ; : : : ; as follows. Set I0 WD f0R g, and for each i 0, define Ii C ai R if Ii C ai R ¨ R; Ii C1 WD Ii otherwise. S1 Finally, set I WD i D0 Ii . Show that I is a maximal ideal of R. Hint: first show that I is an ideal; then show that I ¨ R by assuming that 1R 2 I and deriving a contradiction; finally, show that I is maximal by assuming that for some i D 1; 2; : : : ; we have I ¨ I C ai R ¨ R, and deriving a contradiction. E XERCISE 7.40. Let R be a ring, and let I and J be ideals of R. With the ringtheoretic product as defined in Exercise 7.2, show that: (a) IJ is an ideal; (b) if I and J are principal ideals, with I D aR and J D bR, then IJ D abR, and so is also a principal ideal; (c) IJ I \ J ; (d) if I C J D R, then IJ D I \ J .
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E XERCISE 7.41. Let R be a subring of E, and I an ideal of R. Show that the ring-theoretic product IE is an ideal of E that contains I , and is the smallest such ideal. E XERCISE 7.42. Let M be a maximal ideal of a ring R, and let a; b 2 R. Show that if ab 2 M 2 and b … M , then a 2 M 2 . Here, M 2 WD MM , the ring-theoretic product. E XERCISE 7.43. Let F be a field, and let f 2 F ŒX; Y. Define V .f / WD f.x; y/ 2 F F W f .x; y/ D 0g. Let E WD F ŒX; Y=.f /. (a) Every element ˛ of E naturally defines a function from V .f / to F , as follows: if ˛ D Œgf , with g 2 F ŒX; Y, then for P D .x; y/ 2 V .f /, we define ˛.P / WD g.x; y/. Show that this definition is unambiguous, that is, g h .mod f / implies g.x; y/ D h.x; y/. (b) For P D .x; y/ 2 V .f /, define MP WD f˛ 2 E W ˛.P / D 0g. Show that MP is a maximal ideal of E, and that MP D ECE, where WD ŒX xf and WD ŒY yf . E XERCISE 7.44. Continuing with the previous exercise, now assume that the characteristic of F is not 2, and that f D Y2 , where 2 F ŒX is a non-zero polynomial with no multiple roots in F . (a) Show that if P D .x; y/ 2 V .f /, then so is PN WD .x; y/, and that P D PN ” y D 0 ” .x/ D 0. (b) Let P D .x; y/ 2 V .f / and WD ŒX xf 2 E. Show that E D MP MPN (the ring-theoretic product). Hint: treat the cases P D PN and P ¤ PN separately, and use Exercise 7.42. E XERCISE 7.45. Let R be a ring, and I an ideal of R. Define Rad.I / WD fa 2 R W an 2 I for some positive integer ng. (a) Show that Rad.I / is an ideal. Hint: show that if an 2 I and b m 2 I , then .a C b/nCm 2 I . (b) Show that if R D Z and I D .d /, where d D p1e1 prer is the prime factorization of d , then Rad.I / D .p1 pr /. 7.4 Ring homomorphisms and isomorphisms Definition 7.20. A function from a ring R to a ring R0 is called a ring homomorphism if (i) is a group homomorphism with respect to the underlying additive groups of R and R0 ,
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191
(ii) .ab/ D .a/.b/ for all a; b 2 R, and (iii) .1R / D 1R0 . Expanding the definition, we see that the requirements that must satisfy in order to be a ring homomorphism are that for all a; b 2 R, we have .a C b/ D .a/ C .b/ and .ab/ D .a/.b/; and that .1R / D 1R0 . Note that some texts do not require that a ring homomorphism satisfies part (iii) of our definition (which is not redundant — see Examples 7.49 and 7.50 below). Since a ring homomorphism is also an additive group homomorphism, we use the same notation and terminology for image and kernel. Example 7.41. If S is a subring of a ring R, then the inclusion map i W S ! R is obviously a ring homomorphism. Example 7.42. Suppose I is an ideal of a ring R. Analogous to Example 6.36, we may define the natural map from the ring R to the quotient ring R=I as follows: W R ! R=I a 7! ŒaI : Not only is this a surjective homomorphism of additive groups, with kernel I , it is a ring homomorphism. Indeed, we have .ab/ D ŒabI D ŒaI ŒbI D .a/ .b/; and .1R / D Œ1R I , which is the multiplicative identity in R=I . Example 7.43. For a given positive integer n, the natural map from Z to Zn sends a 2 Z to the residue class Œan . This is a surjective ring homomorphism, whose kernel is nZ. Example 7.44. Let R be a subring of a ring E, and fix ˛ 2 E. The polynomial evaluation map W RŒX ! E g 7! g.˛/ is a ring homomorphism (see Theorem 7.11). The image of consists of all polynomial expressions in ˛ with coefficients in R, and is denoted RŒ˛. As the reader may verify, RŒ˛ is a subring of E containing R and ˛, and is the smallest such subring of E. Example 7.45. We can generalize the previous example to multi-variate polyno-
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mials. If R is a subring of a ring E and ˛1 ; : : : ; ˛n 2 E, then the map W RŒX1 ; : : : ; Xn ! E g 7! g.˛1 ; : : : ; ˛n / is a ring homomorphism. Its image consists of all polynomial expressions in ˛1 ; : : : ; ˛n with coefficients in R, and is denoted RŒ˛1 ; : : : ; ˛n . Moreover, this image is a subring of E containing R and ˛1 ; : : : ; ˛n , and is the smallest such subring of E. Note that RŒ˛1 ; : : : ; ˛n D RŒ˛1 ; : : : ; ˛n 1 Œ˛n . Example 7.46. Let W R ! R0 be a ring homomorphism. We can extend the P P domain of definition of from R to RŒX by defining . i ai Xi / WD i .ai /Xi . This yields a homomorphism from RŒX into R0 ŒX. To verify this, suppose g D P P i i in RŒX. Let s WD g C h 2 RŒX and i ai X and h D i bi X are polynomials P P i p WD gh 2 RŒX, and write s D i si X and p D i pi Xi , so that X si D ai C bi and pi D aj bk : i Dj Ck
Then we have .si / D .ai C bi / D .ai / C .bj /; which is the coefficient of Xi in .g/ C .h/, and X X X .pi / D aj bk D .aj bk / D .aj /.bk /; i Dj Ck
i Dj Ck
i Dj Ck
which is the coefficient of Xi in .g/.h/. Sometimes a more compact notation is convenient: we may prefer to write aN for P the image of a 2 R under , and if we do this, then for g D i ai Xi 2 RŒX, we P write gN for the image i aN i Xi of g under the extension of to RŒX. Example 7.47. Consider the natural map that sends a 2 Z to aN WD Œan 2 Zn (see Example 7.43). As in the previous example, we may extend this to a ring P homomorphism from ZŒX to Zn ŒX, which maps g D i ai Xi 2 ZŒX to gN D P N i Xi 2 Zn ŒX. This homomorphism is clearly surjective. Let us determine its ia P kernel. Observe that if g D i ai Xi , then gN D 0 if and only if n j ai for each i ; therefore, the kernel is the ideal nZŒX of ZŒX. Example 7.48. Let R be a ring of prime characteristic p. For all a; b 2 R, we have (see Exercise 7.1) ! p X p .a C b/p D ap k b k : k kD0
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193
However, by Exercise 1.14, all of the binomial coefficients are multiples of p, except for k D 0 and k D p, and hence in the ring R, all of these terms vanish, leaving us with .a C b/p D ap C b p : This result is often jokingly referred to as the “freshman’s dream,” for somewhat obvious reasons. Of course, as always, we have p
.ab/p D ap b p and 1R D 1R ; and so it follows that the map that sends a 2 R to ap 2 R is a ring homomorphism from R into R. Example 7.49. Suppose R is a non-trivial ring, and let W R ! R map everything in R to 0R . Then satisfies parts (i) and (ii) of Definition 7.20, but not part (iii). Example 7.50. In special situations, part (iii) of Definition 7.20 may be redundant. One such situation arises when W R ! R0 is surjective. In this case, we know that 1R0 D .a/ for some a 2 R, and by part (ii) of the definition, we have .1R / D .1R / 1R0 D .1R /.a/ D .1R a/ D .a/ D 1R0 : For a ring homomorphism W R ! R0 , all of the results of Theorem 6.19 apply. In particular, .0R / D 0R0 , .a/ D .b/ if and only if a b .mod Ker /, and is injective if and only if Ker D f0R g. However, we may strengthen Theorem 6.19 as follows: Theorem 7.21. Let W R ! R0 be a ring homomorphism. (i) If S is a subring of R, then .S / is a subring of R0 ; in particular (setting S WD R), Im is a subring of R0 . (ii) If S 0 is a subring of R0 , then
1 .S 0 /
is a subring of R.
(ii) If I is an ideal of R, then .I / is an ideal of Im . (iv) If I 0 is an ideal of Im , then 1 .I 0 / is an ideal of R; in particular (setting I 0 WD f0R0 g), Ker is an ideal of R. Proof. In each part, we already know that the relevant object is an additive subgroup, and so it suffices to show that the appropriate additional properties are satisfied. (i) For all a; b 2 S , we have ab 2 S , and hence .S / contains .ab/ D .a/.b/. Also, 1R 2 S, and hence .S / contains .1R / D 1R0 . (ii) If .a/ 2 S 0 and .b/ 2 S 0 , then .ab/ D .a/.b/ 2 S 0 . Also, .1R / D 1R 0 2 S 0 .
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(iii) For all a 2 I and r 2 R, we have ar 2 I , and hence .I / contains .ar/ D .a/.r/. (iv) For all a 2 1 .I 0 / and r 2 R, we have .ar/ D .a/.r/, and since .a/ belongs to the ideal I 0 , so does .a/.r/, and hence 1 .I 0 / contains ar. Theorems 6.20 and 6.21 have natural ring analogs — one only has to show that the corresponding group homomorphisms satisfy the additional requirements of a ring homomorphism, which we leave to the reader to verify: Theorem 7.22. If W R ! R0 and 0 W R0 ! R00 are ring homomorphisms, then so is their composition 0 B W R ! R00 . Theorem 7.23. Let i W R ! Ri0 , for i D 1; : : : ; k, be ring homomorphisms. Then the map W R ! R10 Rk0 a 7! .1 .a/; : : : ; k .a// is a ring homomorphism. If a ring homomorphism W R ! R0 is a bijection, then it is called a ring isomorphism of R with R0 . If such a ring isomorphism exists, we say that R is isomorphic to R0 , and write R Š R0 . Moreover, if R D R0 , then is called a ring automorphism on R. Analogous to Theorem 6.22, we have: Theorem 7.24. If is a ring isomorphism of R with R0 , then the inverse function 1 is a ring isomorphism of R0 with R. Proof. Exercise. Because of this theorem, if R is isomorphic to R0 , we may simply say that “R and R0 are isomorphic.” We stress that a ring isomorphism is essentially just a “renaming” of elements; in particular, we have: Theorem 7.25. Let W R ! R0 be a ring isomorphism. (i) For all a 2 R, a is a zero divisor if and only if .a/ is a zero divisor. (ii) For all a 2 R, a is a unit if and only if .a/ is a unit. (iii) The restriction of R to R is a group isomorphism of R with .R0 / . Proof. Exercise. An injective ring homomorphism W R ! E is called an embedding of R in E. In this case, Im is a subring of E and R Š Im . If the embedding is a natural one
7.4 Ring homomorphisms and isomorphisms
195
that is clear from context, we may simply identify elements of R with their images in E under the embedding; that is, for a 2 R, we may simply write “a,” and it is understood that this really means “.a/” if the context demands an element of E. As a slight abuse of terminology, we shall say that R is a subring of E. Indeed, by appropriately renaming elements, we can always make R a subring of E in the literal sense of the term. This practice of identifying elements of a ring with their images in another ring under a natural embedding is very common. We have already seen an example of this, namely, when we formally defined the ring of polynomials RŒX over R in §7.2.1, we defined the map 0 W R ! RŒX that sends c 2 R to the polynomial whose constant term is c, and all other coefficients zero. This map 0 is an embedding, and it was via this embedding that we identified elements of R with elements of RŒX, and so viewed R as a subring of RŒX. We shall see more examples of this later (in particular, Example 7.55 below). Theorems 6.23 and 6.24 also have natural ring analogs — again, one only has to show that the corresponding group homomorphisms are also ring homomorphisms: Theorem 7.26 (First isomorphism theorem). Let W R ! R0 be a ring homomorphism with kernel K and image S 0 . Then we have a ring isomorphism R=K Š S 0 : Specifically, the map N W R=K ! R0 ŒaK 7! .a/ is an injective ring homomorphism whose image is S 0 . Theorem 7.27. Let W R ! R0 be a ring homomorphism. Then for every ideal I of R with I Ker , we may define a ring homomorphism N W R=I ! R0 ŒaI 7! .a/: Moreover, Im N D Im , and N is injective if and only if I D Ker . Example 7.51. Returning again to the Chinese remainder theorem and the discussion in Example 6.48, if fni gkiD1 is a pairwise relatively prime family of positive Q integers, and n WD kiD1 ni , then the map W Z ! Zn1 Znk a 7! .Œan1 ; : : : ; Œank /
196
Rings
is not just a surjective group homomorphism with kernel nZ, it is also a ring homomorphism. Applying Theorem 7.26, we get a ring isomorphism N W
Zn ! Zn1 Znk Œan 7! .Œan1 ; : : : ; Œank /;
which is the same function as the function in Theorem 2.8. By part (iii) of Theorem 7.25, the restriction of to Zn is a group isomorphism of Zn with the multiplicative group of units of Zn1 Znk , which (according to Example 7.15) is Zn1 Znk . Thus, part (iii) of Theorem 2.8 is an immediate consequence of the above observations. Example 7.52. Extending Example 6.49, if n1 and n2 are positive integers with n1 j n2 , then the map N W
Zn2 ! Zn1 Œan2 7! Œan1
is a surjective ring homomorphism. Example 7.53. For a ring R, consider the map W Z ! R that sends m 2 Z to m 1R in R. It is easily verified that is a ring homomorphism. If Ker D f0g, then Im Š Z, and so the ring Z is embedded in R, and R has characteristic zero. If Ker D nZ for some n > 0, then Im Š Zn , and so the ring Zn is embedded in R, and R has characteristic n. Note that Im is the smallest subring of R: any subring of R must contain 1R and be closed under addition and subtraction, and so must contain Im . Example 7.54. We can generalize Example 7.44 by evaluating polynomials at several points. This is most fruitful when the underlying coefficient ring is a field, and the evaluation points belong to the same field. So let F be a field, and let x1 ; : : : ; xk be distinct elements of F . Define the map W F ŒX ! F k g 7! .g.x1 /; : : : ; g.xk //: This is a ring homomorphism. By Theorem 7.13, Ker D .f /, where f WD Qk xi /. By Theorem 7.15, is surjective. Therefore, by Theorem 7.26, we i D1 .X get a ring isomorphism N W F ŒX=.f / ! F k Œgf 7! .g.x1 /; : : : ; g.xk //: Example 7.55. As in Example 7.39, let f be a polynomial over a ring R with deg.f / D ` and lc.f / 2 R , but now assume that ` > 0. Consider the natural
7.4 Ring homomorphisms and isomorphisms
197
map from RŒX to the quotient ring E WD RŒX=.f / that sends g 2 RŒX to Œgf . Let be the restriction of to the subring R of RŒX. Evidently, is a ring homomorphism from R into E. Moreover, since distinct polynomials of degree less than ` belong to distinct residue classes modulo f , we see that is injective. Thus, is an embedding of R into E. As is a very natural embedding, we can identify elements of R with their images in E under , and regard R as a subring P of E. Taking this point of view, we see that if g D i ai Xi , then X X X Œgf D Œ ai Xi f D Œai f .ŒXf /i D ai i D g./; i
i
i
where WD ŒXf 2 E. Therefore, the natural map may be viewed as the polynomial evaluation map (see Example 7.44) that sends g 2 RŒX to g./ 2 E. Note that we have E D RŒ; moreover, every element of E can be expressed uniquely as g./ for some g 2 RŒX of degree less than `, and more generally, for arbitrary g; h 2 RŒX, we have g./ D h./ if and only if g h .mod f /. Finally, note that f ./ D Œf f D Œ0f ; that is, is a root of f . Example 7.56. As a special case of Example 7.55, let f WD X2 C 1 2 RŒX, and consider the quotient ring RŒX=.f /. If we set i WD ŒXf 2 RŒX=.f /, then every element of RŒX=.f / can be expressed uniquely as a C bi, where a; b 2 R. Moreover, we have i 2 D 1, and more generally, for all a; b; a0 ; b 0 2 R, we have .a C bi / C .a0 C b 0 i / D .a C a0 / C .b C b 0 /i and .a C bi / .a0 C b 0 i / D .aa0
bb 0 / C .ab 0 C a0 b/i:
Thus, the rules for arithmetic in RŒX=.f / are precisely the familiar rules of complex arithmetic, and so C and RŒX=.f / are essentially the same, as rings. Indeed, the “algebraically correct” way of defining the complex numbers C is simply to define them to be the quotient ring RŒX=.f / in the first place. This will be our point of view from now on. Example 7.57. Consider the polynomial evaluation map W RŒX ! C D RŒX=.X2 C 1/ that sends g 2 RŒX to g. i /. For every g 2 RŒX, we may write g D .X2 C 1/q C a C bX, where q 2 RŒX and a; b 2 R. Since . i /2 C 1 D i 2 C 1 D 0, we have g. i / D .. i /2 C 1/q. i / C a bi D a bi. Clearly, then, is surjective and the kernel of is the ideal of RŒX generated by the polynomial X2 C 1. By Theorem 7.26, we therefore get a ring automorphism N on C that sends a C bi 2 C to a bi. In fact, N is none other than the complex conjugation map. Indeed, this is the “algebraically correct” way of defining complex conjugation in the first place.
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Rings
Example 7.58. We defined the ring ZŒi of Gaussian integers in Example 7.25 as a subring of C. Let us verify that the notation ZŒi introduced in Example 7.25 is consistent with that introduced in Example 7.44. Consider the polynomial evaluation map W ZŒX ! C that sends g 2 ZŒX to g.i / 2 C. For every g 2 ZŒX, we may write g D .X2 C 1/q C a C bX, where q 2 ZŒX and a; b 2 Z. Since i 2 C 1 D 0, we have g.i / D .i 2 C 1/q.i / C a C bi D a C bi. Clearly, then, the image of is the set fa C bi W a; b 2 Zg, and the kernel of is the ideal of ZŒX generated by the polynomial X2 C 1. This shows that ZŒi in Example 7.25 is the same as ZŒi in Example 7.44, and moreover, Theorem 7.26 implies that ZŒi is isomorphic to ZŒX=.X2 C 1/. Thus, we can directly construct the Gaussian integers as the quotient ring ZŒX=.X2 C 1/. Likewise the field QŒi (see Exercise 7.13) can be constructed directly as QŒX=.X2 C 1/. Example 7.59. Let p be a prime, and consider the quotient ring E WD Zp ŒX=.X2 C 1/. If we set i WD ŒXX2 C1 2 E, then E D Zp Œi D fa C bi W a; b 2 Zp g. In particular, E is a ring of cardinality p 2 . Moreover, we have i 2 D 1, and the rules for addition and multiplication in E look exactly the same as they do in C: for all a; b; a0 ; b 0 2 Zp , we have .a C bi / C .a0 C b 0 i / D .a C a0 / C .b C b 0 /i and .a C bi / .a0 C b 0 i / D .aa0
bb 0 / C .ab 0 C a0 b/i:
The ring E may or may not be a field. We now determine for which primes p we get a field. If p D 2, then 0 D 1 C i 2 D .1 C i /2 (see Example 7.48), and so in this case, 1 C i is a zero divisor and E is not a field. Now suppose p is odd. There are two subcases: p 1 .mod 4/ and p 3 .mod 4/. Suppose p 1 .mod 4/. By Theorem 2.31, there exists c 2 Zp such that 2 c D 1, and therefore X2 C1 D X2 c 2 D .X c/.XCc/, and by Example 7.45, we have ring isomorphism E Š Zp Zp (which maps aCbi 2 E to .aCbc; a bc/ 2 Zp Zp ); in particular, E is not a field. Indeed, c C i is a zero divisor, since .c C i/.c i/ D c 2 i 2 D c 2 C 1 D 0. Suppose p 3 .mod 4/. By Theorem 2.31, there is no c 2 Zp such that c 2 D 1. It follows that for all a; b 2 Zp , not both zero, we must have a2 C b 2 ¤ 0; indeed, suppose that a2 C b 2 D 0, and that, say, b ¤ 0; then we would have .a=b/2 D 1, contradicting the assumption that 1 has no square root in Zp . Therefore, a2 C b 2 has a multiplicative inverse in Zp , from which it follows that
7.4 Ring homomorphisms and isomorphisms
199
the formula for multiplicative inverses in C applies equally well in E; that is, .a C bi /
1
D
a bi : a2 C b 2
Therefore, in this case, E is a field. In Example 7.40, we saw a finite field of cardinality 4. The previous example provides us with an explicit construction of a finite field of cardinality p 2 , for every prime p congruent to 3 modulo 4. As the next example shows, there exist finite fields of cardinality p 2 for all primes p. Example 7.60. Let p an odd prime, and let d 2 Zp . Let f WD X2 d 2 Zp ŒX, and consider the ring E WD Zp ŒX=.f / D Zp Œ, where WD ŒXf 2 E. We have E D fa C b W a; b 2 Zp g and jEj D p 2 . Note that 2 D d , and the general rules for arithmetic in E look like this: for all a; b; a0 ; b 0 2 Zp , we have .a C b/ C .a0 C b 0 / D .a C a0 / C .b C b 0 / and .a C b/ .a0 C b 0 / D .aa0 C bb 0 d / C .ab 0 C a0 b/: Suppose that d 2 .Zp /2 , so that d D c 2 for some c 2 Zp . Then f D .X c/.XCc/, and like in previous example, we have a ring isomorphism E Š Zp Zp (which maps a C b 2 E to .a C bc; a bc/ 2 Zp Zp ); in particular, E is not a field. Suppose that d … .Zp /2 . This implies that for all a; b 2 Zp , not both zero, we have a2 b 2 d ¤ 0. Using this, we get the following formula for multiplicative inverses in E: a b .a C b/ 1 D 2 : a b2d Therefore, E is a field in this case. By Theorem 2.20, we know that j.Zp /2 j D .p 1/=2, so there exists d 2 Zp n .Zp /2 for all odd primes p. Thus, we have a general (though not explicit) construction for finite fields of cardinality p 2 for all odd primes p. E XERCISE 7.46. Show that if W F ! R is a ring homomorphism from a field F into a ring R, then either R is trivial or is injective. Hint: use Exercise 7.24. E XERCISE 7.47. Verify that the “is isomorphic to” relation on rings is an equivalence relation; that is, for all rings R1 ; R2 ; R3 , we have: (a) R1 Š R1 ; (b) R1 Š R2 implies R2 Š R1 ;
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Rings
(c) R1 Š R2 and R2 Š R3 implies R1 Š R3 . E XERCISE 7.48. Let i W Ri ! Ri0 , for i D 1; : : : ; k, be ring homomorphisms. Show that the map W R1 Rk ! R10 Rk0 .a1 ; : : : ; ak / 7! .1 .a1 /; : : : ; k .ak // is a ring homomorphism. E XERCISE 7.49. Let W R ! R0 be a ring homomorphism, and let a 2 R. Show that .aR/ D .a/.R/. E XERCISE 7.50. Let W R ! R0 be a ring homomorphism. Let S be a subring of R, and let W S ! R0 be the restriction of to S. Show that is a ring homomorphism and that Ker D Ker \ S . E XERCISE 7.51. Suppose R1 ; : : : ; Rk are rings. Show that for each i D 1; : : : ; k, the projection map i W R1 Rk ! Ri that sends .a1 ; : : : ; ak / to ai is a surjective ring homomorphism. E XERCISE 7.52. Show that if R D R1 R2 for rings R1 and R2 , and I1 is an ideal R1 and I2 is an ideal of R2 , then we have a ring isomorphism R=.I1 I2 / Š R1 =I1 R2 =I2 . E XERCISE 7.53. Let I be an ideal of R, and S a subring of R. As we saw in Exercises 7.27, and 7.28, I \ S is an ideal of S , and I is an ideal of the subring I C S . Show that we have a ring isomorphism .I C S /=I Š S=.I \ S /. E XERCISE 7.54. Let W R ! R0 be a ring homomorphism with kernel K. Let I be an ideal of R. Show that we have a ring isomorphism R=.I CK/ Š .R/=.R/. E XERCISE 7.55. Let n be a positive integer, and consider the natural map that sends a 2 Z to aN WD Œan 2 Zn , which we may extend coefficient-wise to a ring homomorphism from ZŒX to Zn ŒX, as in Example 7.47. Show that for every f 2 ZŒX, we have a ring isomorphism ZŒX=.f; n/ Š Zn ŒX=.fN/. E XERCISE 7.56. Let n be a positive integer. Show that we have ring isomorphisms ZŒX=.n/ Š Zn ŒX, ZŒX=.X/ Š Z, and ZŒX=.X; n/ Š Zn . E XERCISE 7.57. Let n D pq, where p and q are distinct primes. Show that we have a ring isomorphism Zn ŒX Š Zp ŒX Zq ŒX. E XERCISE 7.58. Let p be a prime with p 1 .mod 4/. Show that we have a ring isomorphism ZŒX=.X2 C 1; p/ Š Zp Zp .
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201
E XERCISE 7.59. Let W R ! R0 be a surjective ring homomorphism. Let S be the set of all ideals of R that contain Ker , and let S 0 be the set of all ideals of R0 . Show that the sets S and S 0 are in one-to-one correspondence, via the map that sends I 2 S to .I / 2 S 0 . Moreover, show that under this correspondence, prime ideals in S correspond to prime ideals in S 0 , and maximal ideals in S correspond to maximal ideals in S 0 . (See definitions above Exercise 7.37.) E XERCISE 7.60. Let n be a positive integer whose factorization into primes is n D p1e1 prer . What are the prime ideals of Zn ? (See definitions above Exercise 7.37.) E XERCISE 7.61. Let W R ! S be a ring homomorphism. Show that .R / S , and that the restriction of to R yields a group homomorphism W R ! S . E XERCISE 7.62. Let R be a ring, and let x1 ; : : : ; xn be elements of R. Show that the rings R and RŒX1 ; : : : ; Xn =.X1 x1 ; : : : ; Xn xn / are isomorphic. E XERCISE 7.63. This exercise and the next generalize the Chinese remainder theorem to arbitrary rings. Suppose I and J are two ideals of a ring R such that I C J D R. Show that the map W R ! R=I R=J that sends a 2 R to .ŒaI ; ŒaJ / is a surjective ring homomorphism with kernel IJ (see Exercise 7.40). Conclude that R=.IJ / is isomorphic to R=I R=J . E XERCISE 7.64. Generalize the previous exercise, showing that R=.I1 Ik / is isomorphic to R=I1 R=Ik , where R is a ring, and I1 ; : : : ; Ik are ideals of R, provided Ii C Ij D R for all i; j such that i ¤ j . E XERCISE 7.65. Let Q.m/ be the subring of Q defined in Example 7.26. Let us define the map W Q.m/ ! Zm as follows. For a=b 2 Q with b relatively prime to m, .a=b/ WD Œam .Œbm / 1 . Show that is unambiguously defined, and is a surjective ring homomorphism. Also, describe the kernel of . E XERCISE 7.66. Let R be a ring, a 2 R , and b 2 R. Define the map W RŒX ! RŒX that sends g 2 RŒX to g aX C b . Show that is a ring automorphism. E XERCISE 7.67. Consider the subring ZŒ1=2 of Q. Show that ZŒ1=2 D fa=2i W a; i 2 Z; i 0g, that .ZŒ1=2/ D f2i W i 2 Zg, and that every non-zero ideal of ZŒ1=2 is of the form .m/, for some uniquely determined, odd integer m.
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7.5 The structure of Zn We are now in a position to precisely characterize the structure of the group Zn , for an arbitrary integer n > 1. This characterization will prove to be very useful in a number of applications. Suppose n D p1e1 prer is the factorization of n into primes. By the Chinese remainder theorem (see Theorem 2.8 and Example 7.51), we have the ring isomorphism W
Zn ! Zpe1 Zprer 1
Œan 7! .Œape1 ; : : : ; Œaprer /; 1
and restricting to
Zn
yields a group isomorphism Zn Š Ze1 Zper : p1
r
Thus, to determine the structure of the group Zn for general n, it suffices to determine the structure for n D p e , where p is prime. By Theorem 2.10, we already know the order of the group Zpe , namely, .p e / D p e 1 .p 1/. The main result of this section is the following: Theorem 7.28. If p is an odd prime, then for every positive integer e, the group Zpe is cyclic. The group Z2e is cyclic for e D 1 or 2, but not for e 3. For e 3, Z2e is isomorphic to the additive group Z2 Z2e 2 . In the case where e D 1, this theorem is a special case the following, more general, theorem: Theorem 7.29. Let D be an integral domain and G a subgroup of D of finite order. Then G is cyclic. Proof. Suppose G is not cyclic. Then by Theorem 6.40, we know that the exponent m of G is strictly less than jGj. It follows that am D 1 for all a 2 G. That is, all the elements of G are roots of the polynomial Xm 1 2 DŒX. But by Theorem 7.14, a polynomial of degree m over an integral domain has at most m roots, and this contradicts the fact that m < jGj. This theorem immediately implies that Zp is cyclic for every prime p, since Zp is a field; however, we cannot directly use this theorem to prove that Zpe is cyclic for e > 1 (and p odd), because Zpe is not a field. To deal with the case e > 1, we need a few simple facts. Lemma 7.30. Let p be a prime. For every positive integer e, if a b .mod p e /, then ap b p .mod p eC1 /.
7.5 The structure of Zn
203
Proof. We have a D b Ccp e for some c 2 Z. Thus, ap D b p Cpb p for an integer d . It follows that ap b p .mod p eC1 /.
1 cp e Cdp 2e
Lemma 7.31. Let p be a prime, and let e be a positive integer such that p e > 2. If a 1 C p e .mod p eC1 /, then ap 1 C p eC1 .mod p eC2 /. Proof. By Lemma 7.30, ap .1 C p e /p .mod p eC2 /. Expanding .1 C p e /p , we have ! p X1 p p ek C p ep : .1 C p e /p D 1 C p p e C k kD2
By Exercise 1.14, all of the terms in the sum on k are divisible by p 1C2e , and 1 C 2e e C 2 for all e 1. For the term p ep , the assumption that p e > 2 means that either p 3 or e 2, which implies ep e C 2. Now consider Theorem 7.28 in the case where p is odd. As we already know that Zp is cyclic, assume e > 1. Let x 2 Z be chosen so that Œxp generates Zp . Suppose the multiplicative order of Œxpe 2 Zpe is m. Then as x m 1 .mod p e / implies x m 1 .mod p/, it must be the case that p 1 divides m, and so Œx m=.p 1/ pe has multiplicative order exactly p 1. By Theorem 6.38, if we find an integer y such that Œype has multiplicative order p e 1 , then Œx m=.p 1/ ype has multiplicative order .p 1/p e 1 , and we are done. We claim that y WD 1 C p does the job. Any integer between 0 and p e 1 can be expressed as an e-digit number in base p; for example, y D .0 0 1 1/p . If we compute successive pth powers of y modulo p e , then by Lemma 7.31 we have y mod p e D .0 y p mod p e D . 2 y p mod p e D . :: : e 2
yp e yp
1
0 1 1/p ; 1 0 1/p ; 1 0 0 1/p ;
mod p e D .1 0 mod p e D .0
0 1/p ; 0 1/p :
Here, “” indicates an arbitrary digit. From this table of values, it is clear (see Theorem 6.37) that Œype has multiplicative order p e 1 . That proves Theorem 7.28 for odd p. We now prove Theorem 7.28 in the case p D 2. For e D 1 and e D 2, the theorem is easily verified. Suppose e 3. Consider the subgroup G Z2e generated by Œ52e . Expressing integers between 0 and 2e 1 as e-digit binary
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Rings
numbers, and applying Lemma 7.31, we have 5 mod 2e D .0 52 mod 2e D . :: : e 3
52 e 52
2
0 1 0 1/2 ; 1 0 0 1/2 ;
mod 2e D .1 0 mod 2e D .0
0 1/2 ; 0 1/2 :
So it is clear (see Theorem 6.37) that Œ52e has multiplicative order 2e 2 . We claim that Œ 12e … G. If it were, then since it has multiplicative order 2, and since every cyclic group of even order has precisely one element of order 2 (see Theorem 6.32), e 3 it must be equal to Œ52 2e ; however, it is clear from the above calculation that e 3 52 6 1 .mod 2e /. Let H Z2e be the subgroup generated by Œ 12e . Then from the above, G \ H D fŒ12e g, and hence by Theorem 6.25, G H is isomorphic to the subgroup G H of Z2e . But since the orders of G H and Z2e are equal, we must have G H D Z2e . That proves the theorem. Example 7.61. Let p be an odd prime, and let d be a positive integer dividing p 1. Since Zp is a cyclic group of order p 1, Theorem 6.32, implies that .Zp /d is the unique subgroup of Zp of order .p 1/=d , and moreover, .Zp /d D Zp f.p 1/=d g; that is, for all ˛ 2 Zp , we have ˛ D ˇ d for some ˇ 2 Zp ” ˛ .p
1/=d
D 1:
Setting d D 2, we arrive again at Euler’s criterion (Theorem 2.21), but by a very different, and perhaps more elegant, route than that taken in our original proof of that theorem. E XERCISE 7.68. Show that if n is a positive integer, the group Zn is cyclic if and only if n D 1; 2; 4; p e ; or 2p e ; where p is an odd prime and e is a positive integer. E XERCISE 7.69. Let n D pq, where p and q are distinct primes such that p D 2p 0 C 1 and q D 2q 0 C 1, where p 0 and q 0 are themselves prime. Show that the subgroup .Zn /2 of squares is a cyclic group of order p 0 q 0 . E XERCISE 7.70. Let n D pq, where p and q are distinct primes such that p − .q 1/ and q − .p 1/. (a) Show that the map that sends Œan 2 Zn to Œan n2 2 .Zn2 /n is a group isomorphism (in particular, you need to show that this map is unambiguously defined).
7.5 The structure of Zn
205
(b) Consider the element ˛ WD Œ1 C nn2 2 Zn2 ; show that for every nonnegative integer k, ˛ k D Œ1 C k nn2 ; deduce that ˛ has multiplicative order n, and also that the identity ˛ k D Œ1 C k nn2 holds for all integers k. (c) Show that the map from Zn Zn to Zn2 that sends .Œkn ; Œan / to Œ.1 C k n/an n2 is a group isomorphism. E XERCISE 7.71. This exercise develops an alternative proof of Theorem 7.29 that relies on less group theory. Let n be the order of the group. Using Theorem 7.14, show that for all d j n, there are at most d elements in the group whose multiplicative order divides d . From this, deduce that for all d j n, the number of elements of multiplicative order d is either 0 or .d /. Now use Theorem 2.40 to deduce that for all d j n (and in particular, for d D n), the number of elements of multiplicative order d is equal to .d /.
8 Finite and discrete probability distributions
To understand the algorithmic aspects of number theory and algebra, and applications such as cryptography, a firm grasp of the basics of probability theory is required. This chapter introduces concepts from probability theory, starting with the basic notions of probability distributions on finite sample spaces, and then continuing with conditional probability and independence, random variables, and expectation. Applications such as “balls and bins,” “hash functions,” and the “leftover hash lemma” are also discussed. The chapter closes by extending the basic theory to probability distributions on countably infinite sample spaces. 8.1 Basic definitions Let ˝ be a finite, non-empty set. A probability distribution on ˝ is a function P W ˝ ! Œ0; 1 that satisfies the following property:
X
P.!/ D 1:
(8.1)
!2˝
The set ˝ is called the sample space of P. Intuitively, the elements of ˝ represent the possible outcomes of a random experiment, where the probability of outcome ! 2 ˝ is P.!/. For now, we shall only consider probability distributions on finite sample spaces. Later in this chapter, in §8.10, we generalize this to allow probability distributions on countably infinite sample spaces. Example 8.1. If we think of rolling a fair die, then setting ˝ WD f1; 2; 3; 4; 5; 6g, and P.!/ WD 1=6 for all ! 2 ˝, gives a probability distribution that naturally describes the possible outcomes of the experiment. Example 8.2. More generally, if ˝ is any non-empty, finite set, and P.!/ WD 1=j˝j for all ! 2 ˝, then P is called the uniform distribution on ˝. 206
8.1 Basic definitions
207
Example 8.3. A coin toss is an example of a Bernoulli trial, which in general is an experiment with only two possible outcomes: success, which occurs with probability p, and failure, which occurs with probability q WD 1 p. Of course, success and failure are arbitrary names, which can be changed as convenient. In the case of a coin, we might associate success with the outcome that the coin comes up heads. For a fair coin, we have p D q D 1=2; for a biased coin, we have p ¤ 1=2. An event is a subset A of ˝, and the probability of A is defined to be X PŒA WD P.!/:
(8.2)
!2A
While an event is simply a subset of the sample space, when discussing the probability of an event (or other properties to be introduced later), the discussion always takes place relative to a particular probability distribution, which may be implicit from context. For events A and B, their union A [ B logically represents the event that either the event A or the event B occurs (or both), while their intersection A \ B logically represents the event that both A and B occur. For an event A, we define its complement Ax WD ˝ n A, which logically represents the event that A does not occur. In working with events, one makes frequent use of the usual rules of Boolean logic. DeMorgan’s law says that for all events A and B, x A [ B D Ax \ Bx and A \ B D Ax [ B: We also have the Boolean distributive law: for all events A; B; and C, A \ .B [ C/ D .A \ B/ [ .A \ C/ and A [ .B \ C/ D .A [ B/ \ .A [ C/: Example 8.4. Continuing with Example 8.1, the event that the die has an odd value is A WD f1; 3; 5g, and we have PŒA D 1=2. The event that the die has a value greater than 2 is B WD f3; 4; 5; 6g, and PŒB D 2=3. The event that the die x D 1=3. The event that the has a value that is at most 2 is Bx D f1; 2g, and PŒB value of the die is odd or exceeds 2 is A [ B D f1; 3; 4; 5; 6g, and PŒA [ B D 5=6. The event that the value of the die is odd and exceeds 2 is A \ B D f3; 5g, and PŒA \ B D 1=3. Example 8.5. If P is the uniform distribution on a set ˝, and A is a subset of ˝, then PŒA D jAj=j˝j. We next derive some elementary facts about probabilities of certain events, and
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Finite and discrete probability distributions
relations among them. It is clear from the definitions that PŒ; D 0 and PŒ˝ D 1;
and that for every event A, we have x D1 PŒA
PŒA:
Now consider events A and B, and their union A [ B. We have PŒA [ B PŒA C PŒBI
(8.3)
PŒA [ B D PŒA C PŒB if A and B are disjoint;
(8.4)
moreover,
that is, if A \ B D ;. The exact formula for arbitrary events A and B is: PŒA [ B D PŒA C PŒB
PŒA \ B:
(8.5)
(8.3), (8.4), and (8.5) all follow from the observation that in the expression X X PŒA C PŒB D P.!/ C P.!/; !2A
!2B
the value P.!/ is counted once for each ! 2 A [ B, except for those ! 2 A \ B, for which P.!/ is counted twice. Example 8.6. Alice rolls two dice, and asks Bob to guess a value that appears on either of the two dice (without looking). Let us model this situation by considering the uniform distribution on ˝ WD f1; : : : ; 6g f1; : : : ; 6g, where for each pair .s; t / 2 ˝, s represents the value of the first die, and t the value of the second. For k D 1; : : : ; 6, let Ak be the event that the first die is k, and Bk the event that the second die is k. Let Ck D Ak [ Bk be the event that k appears on either of the two dice. No matter what value k Bob chooses, the probability that this choice is correct is PŒCk D PŒAk [ Bk D PŒAk C PŒBk
D 1=6 C 1=6
PŒAk \ Bk
1=36 D 11=36;
which is slightly less than the estimate PŒAk C PŒBk obtained from (8.3). If fAi gi2I is a family of events, indexed by some set I , we can naturally form S T the union i 2I Ai and intersection i 2I Ai . If I D ;, then by definition, the union is ;, and by special convention, the intersection is the entire sample space ˝.
209
8.1 Basic definitions
Logically, the union represents the event that some Ai occurs, and the intersection represents the event that all the Ai ’s occur. DeMorgen’s law generalizes as follows: [ \ \ [ Ai D Ai D Axi and Axi ; i 2I
i 2I
i 2I
i 2I
and if B is an event, then the Boolean distributive law generalizes as follows: \ \ [ [ .B [ Ai /: .B \ Ai / and B [ Ai D B\ Ai D i 2I
i 2I
i 2I
i 2I
We now generalize (8.3), (8.4), and (8.5) from pairs of events to families of events. Let fAi gi 2I be a finite family of events (i.e., the index set I is finite). Using (8.3), it follows by induction on jI j that h[ i X PŒAi ; (8.6) P Ai i 2I
i 2I
which is known as Boole’s inequality (and also sometimes called the union bound). Analogously, using (8.4), it follows by induction on jI j that h[ i X P Ai D PŒAi ; if fAi gi 2I is pairwise disjoint; (8.7) i 2I
i 2I
that is, if Ai \Aj D ; for all i; j 2 I with i ¤ j . We shall refer to (8.7) as Boole’s equality. Both (8.6) and (8.7) are invaluable tools in calculating or estimating the probability of an event A by breaking A up into a family fAi gi 2I of smaller, and hopefully simpler, events, whose union is A. We shall make frequent use of them. The generalization of (8.5) is messier. Consider first the case of three events, A, B, and C. We have PŒA [ B [ C D PŒA C PŒB C PŒC
PŒA \ B
PŒA \ C
PŒB \ C
C PŒA \ B \ C: Thus, starting with the sum of the probabilities of the individual events, we have to subtract a “correction term” that consists of the sum of probabilities of all intersections of pairs of events; however, this is an “over-correction,” and we have to correct the correction by adding back in the probability of the intersection of all three events. The general statement is as follows: Theorem 8.1 (Inclusion/exclusion principle). Let fAi gi 2I be a finite family of events. Then h[ i h\ i X P Ai D . 1/jJ j 1 P Aj ; i 2I
;¨J I
the sum being over all non-empty subsets J of I .
j 2J
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Finite and discrete probability distributions
Proof. For ! 2 ˝ and B ˝, define ı! ŒB WD 1 if ! 2 B, and ı! ŒB WD 0 if ! … B. As a function of !, ı! ŒB is simply the characteristic function of B. One may x D 1 ı! ŒB easily verify that for all ! 2 ˝, B ˝, and C ˝, we have ı! ŒB and ı! ŒB \ C D ı! ŒBı! ŒC. It is also easily seen that for every B ˝, we have P ŒB D PŒB. !2˝ P.!/ı S! T Let A WD i 2I Ai , and for J I , let AJ WD j 2J Aj . For every ! 2 ˝, h\ i Y Y x D ı! 1 ı! ŒA D ı! ŒA Axi D ı! ŒAxi D .1 ı! ŒAi / i 2I
D
X
. 1/
Y
jJ j
i 2I
ı! ŒAi D
jJ j
. 1/
ı! ŒAJ ;
J I
j 2J
J I
i 2I
X
and so X
ı! ŒA D
. 1/jJ j
1
ı! ŒAJ :
(8.8)
;¨J I
Multiplying (8.8) by P.!/, and summing over all ! 2 ˝, we have X X X . 1/jJ j 1 ı! ŒAJ P.!/ P.!/ı! ŒA D PŒA D !2˝
!2˝
X
D
. 1/jJ j
1
X
;¨J I
!2˝
;¨J I
X
P.!/ı! ŒAJ D
. 1/jJ j
1
PŒAJ :
;¨J I
One can also state the inclusion/exclusion principle in a slightly different way, splitting the sum into terms with jJ j D 1, jJ j D 2, etc., as follows: h[
P
i 2I
i
Ai D
X i 2I
PŒAi C
jI j X
. 1/k
kD2
1
X J I jJ jDk
h\
P
i Aj ;
j 2J
where the last sum in this formula is taken over all subsets J of I of size k. We next consider a useful way to “glue together” probability distributions. Suppose one conducts two physically separate and unrelated random experiments, with each experiment modeled separately as a probability distribution. What we would like is a way to combine these distributions, obtaining a single probability distribution that models the two experiments as one grand experiment. This can be accomplished in general, as follows. Let P1 W ˝1 ! Œ0; 1 and P2 W ˝2 ! Œ0; 1 be probability distributions. Their product distribution P WD P1 P2 is defined as follows: P W ˝1 ˝2 ! Œ0; 1
.!1 ; !2 / 7! P1 .!1 /P2 .!2 /:
211
8.1 Basic definitions
It is easily verified that P is a probability distribution on the sample space ˝1 ˝2 : X X X X P.!1 ; !2 / D P1 .!1 /P2 .!2 / D P1 .!1 / P2 .!2 / D 11 D 1: !1 ;!2
!1 ;!2
!1
!2
More generally if Pi W ˝i ! Œ0; 1, for i D 1; : : : ; n, are probability distributions, then their product distribution is P WD P1 Pn , where P W ˝1 ˝n ! Œ0; 1
.!1 ; : : : ; !n / 7! P1 .!1 / Pn .!n /: If P1 D P2 D D Pn , then we may write P D Pn1 . It is clear from the definitions that if each Pi is the uniform distribution on ˝i , then P is the uniform distribution on ˝1 ˝n . Example 8.7. We can view the probability distribution P in Example 8.6 as P21 , where P1 is the uniform distribution on f1; : : : ; 6g. Example 8.8. Suppose we have a coin that comes up heads with some probability p, and tails with probability q WD 1 p. We toss the coin n times, and record the outcomes. We can model this as the product distribution P D Pn1 , where P1 is the distribution of a Bernoulli trial (see Example 8.3) with success probability p, and where we identify success with heads, and failure with tails. The sample space ˝ of P is the set of all 2n tuples ! D .!1 ; : : : ; !n /, where each !i is either heads or tails. If the tuple ! has k heads and n k tails, then P.!/ D p k q n k , regardless of the positions of the heads and tails in the tuple. For each k D 0; : : : ; n, let Ak be the event that our coin comes up heads exactly k times. As a set, Ak consists of all those tuples in the sample space with exactly k heads, and so ! n jAk j D ; k from which it follows that ! n k n PŒAk D p q k
k
:
If our coin is a fair coin, so that p D q D 1=2, then P is the uniform distribution on ˝, and for each k D 0; : : : ; n, we have ! n PŒAk D 2 n: k Suppose P W ˝ ! Œ0; 1 is a probability distribution. The support of P is defined to be the set f! 2 ˝ W P.!/ ¤ 0g. Now consider another probability
212
Finite and discrete probability distributions
distribution P0 W ˝ 0 ! Œ0; 1. Of course, these two distributions are equal if and only if ˝ D ˝ 0 and P.!/ D P0 .!/ for all ! 2 ˝. However, it is natural and convenient to have a more relaxed notion of equality. We shall say that P and P0 are essentially equal if the restriction of P to its support is equal to the restriction of P0 to its support. For example, if P the probability distribution on f1; 2; 3; 4g that assigns probability 1=3 to 1, 2, and 3, and probability 0 to 4, we may say that P is essentially the uniform distribution on f1; 2; 3g. E XERCISE 8.1. Show that PŒA \ BPŒA [ B PŒAPŒB for all events A; B. x Show that E XERCISE 8.2. Suppose A; B; C are events such that A \ Cx D B \ C. jPŒA PŒBj PŒC. E XERCISE 8.3. Let m be a positive integer, and let ˛.m/ be the probability that a number chosen at random from f1; : : : ; mg is divisible by either 4, 5, or 6. Write down an exact formula for ˛.m/, and also show that ˛.m/ D 14=30 C O.1=m/. E XERCISE 8.4. This exercise asks you to generalize Boole’s inequality (8.6), proving Bonferroni’s inequalities. Let fAi gi2I be a finite family of events, where n WD jI j. For m D 0; : : : ; n, define ˛m WD
m X
. 1/k
kD1
1
X
h\
P
J I jJ jDk
i Aj :
j 2J
Also, define h[ i ˛ WD P Ai : i 2I
Show that ˛ ˛m if m is odd, and ˛ ˛m if m is even. Hint: use induction on n. 8.2 Conditional probability and independence Let P be a probability distribution on a sample space ˝. For a given event B ˝ with PŒB ¤ 0, and for ! 2 ˝, let us define P.!/=PŒB if ! 2 B, P.! j B/ WD 0 otherwise. Viewing B as fixed, the function P. j B/ is a new probability distribution on the sample space ˝, called the conditional distribution (derived from P) given B. Intuitively, P. j B/ has the following interpretation. Suppose a random experiment produces an outcome according to the distribution P. Further, suppose we
8.2 Conditional probability and independence
213
learn that the event B has occurred, but nothing else about the outcome. Then the distribution P. j B/ assigns new probabilities to all possible outcomes, reflecting the partial knowledge that the event B has occurred. For a given event A ˝, its probability with respect to the conditional distribution given B is X PŒA \ B PŒA j B D P.! j B/ D : PŒB !2A
The value PŒA j B is called the conditional probability of A given B. Again, the intuition is that this is the probability that the event A occurs, given the partial knowledge that the event B has occurred. For events A and B, if PŒA\B D PŒA PŒB, then A and B are called independent events. If PŒB ¤ 0, a simple calculation shows that A and B are independent if and only if PŒA j B D PŒA; intuitively, independence means that the partial knowledge that event B has occurred does not affect the likelihood that A occurs. Example 8.9. Suppose P is the uniform distribution on ˝, and that B ˝ with PŒB ¤ 0. Then the conditional distribution given B is essentially the uniform distribution on B. Example 8.10. Consider again Example 8.4, where A is the event that the value on the die is odd, and B is the event that the value of the die exceeds 2. Then as we calculated, PŒA D 1=2, PŒB D 2=3, and PŒA \ B D 1=3; thus, PŒA \ B D PŒAPŒB, and we conclude that A and B are independent. Indeed, PŒA j B D .1=3/=.2=3/ D 1=2 D PŒA; intuitively, given the partial knowledge that the value on the die exceeds 2, we know it is equally likely to be either 3, 4, 5, or 6, and so the conditional probability that it is odd is 1=2. However, consider the event C that the value on the die exceeds 3. We have PŒC D 1=2 and PŒA \ C D 1=6 ¤ 1=4, from which we conclude that A and C are not independent. Indeed, PŒA j C D .1=6/=.1=2/ D 1=3 ¤ PŒA; intuitively, given the partial knowledge that the value on the die exceeds 3, we know it is equally likely to be either 4, 5, or 6, and so the conditional probability that it is odd is just 1=3, and not 1=2. Example 8.11. In Example 8.6, suppose that Alice tells Bob the sum of the two dice before Bob makes his guess. The following table is useful for visualizing the situation:
214
Finite and discrete probability distributions 6 5 4 3 2 1
7 6 5 4 3 2 1
8 7 6 5 4 3 2
9 8 7 6 5 4 3
10 9 8 7 6 5 4
11 10 9 8 7 6 5
12 11 10 9 8 7 6
For example, suppose Alice tells Bob the sum is 4. Then what is Bob’s best strategy in this case? Let D` be the event that the sum is `, for ` D 2; : : : ; 12, and consider the conditional distribution given D4 . This conditional distribution is essentially the uniform distribution on the set f.1; 3/; .2; 2/; .3; 1/g. The numbers 1 and 3 both appear in two pairs, while the number 2 appears in just one pair. Therefore, PŒC1 j D4 D PŒC3 j D4 D 2=3;
while PŒC2 j D4 D 1=3
and PŒC4 j D4 D PŒC5 j D4 D PŒC6 j D4 D 0:
Thus, if the sum is 4, Bob’s best strategy is to guess either 1 or 3, which will be correct with probability 2=3. Similarly, if the sum is 5, then we consider the conditional distribution given D5 , which is essentially the uniform distribution on f.1; 4/; .2; 3/; .3; 2/; .4; 1/g. In this case, Bob should choose one of the numbers k D 1; : : : ; 4, each of which will be correct with probability PŒCk j D5 D 1=2. Suppose fBi gi 2I is a finite, pairwise disjoint family of events, whose union is ˝. Now consider an arbitrary event A. Since fA \ Bi gi 2I is a pairwise disjoint family of events whose union is A, Boole’s equality (8.7) implies X PŒA D PŒA \ Bi : (8.9) i2I
Furthermore, if each Bi occurs with non-zero probability (so that in particular, fBi gi 2I is a partition of ˝), then we have X PŒA D PŒA j Bi PŒBi : (8.10) i 2I
If, in addition, PŒA ¤ 0, then for each j 2 I , we have PŒBj j A D
PŒA \ Bj PŒA
DP
PŒA j Bj PŒBj
i 2I
PŒA j Bi PŒBi
:
(8.11)
Equations (8.9) and (8.10) are sometimes called the law of total probability, while
8.2 Conditional probability and independence
215
equation (8.11) is known as Bayes’ theorem. Equation (8.10) (resp., (8.11)) is useful for computing or estimating PŒA (resp., PŒBj j A) by conditioning on the events Bi . Example 8.12. Let us continue with Example 8.11, and compute Bob’s overall probability of winning, assuming he follows an optimal strategy. If the sum is 2 or 12, clearly there is only one sensible choice for Bob to make, and it will certainly be correct. If the sum is any other number `, and there are N` pairs in the sample space that sum to that number, then there will always be a value that appears in exactly 2 of these N` pairs, and Bob should choose such a value (see the diagram in Example 8.11). Indeed, this is achieved by the simple rule of choosing the value 1 if ` 7, and the value 6 if ` > 7. This is an optimal strategy for Bob, and if C is the event that Bob wins following this strategy, then by total probability (8.10), we have 12 X PŒC D PŒC j D` PŒD` : `D2
Moreover, 1 1 1 1 D ; PŒC j D12 PŒD12 D 1 D ; 36 36 36 36 and for ` D 3; : : : ; 11, we have PŒC j D2 PŒD2 D 1
PŒC j D` PŒD` D
2 N` 1 D : N` 36 18
Therefore, PŒC D
1 1 9 10 C C D : 36 36 18 18
Example 8.13. Suppose that the rate of incidence of disease X in the overall population is 1%. Also suppose that there is a test for disease X ; however, the test is not perfect: it has a 5% false positive rate (i.e., 5% of healthy patients test positive for the disease), and a 2% false negative rate (i.e., 2% of sick patients test negative for the disease). A doctor gives the test to a patient and it comes out positive. How should the doctor advise his patient? In particular, what is the probability that the patient actually has disease X, given a positive test result? Amazingly, many trained doctors will say the probability is 95%, since the test has a false positive rate of 5%. However, this conclusion is completely wrong. Let A be the event that the test is positive and let B be the event that the patient has disease X. The relevant quantity that we need to estimate is PŒB j A; that is, the probability that the patient has disease X , given a positive test result. We use
216
Finite and discrete probability distributions
Bayes’ theorem to do this: PŒB j A D
PŒA j BPŒB
x PŒB x PŒA j BPŒB C PŒA j B
D
0:98 0:01 0:17: 0:98 0:01 C 0:05 0:99
Thus, the chances that the patient has disease X given a positive test result are just 17%. The correct intuition here is that it is much more likely to get a false positive than it is to actually have the disease. Of course, the real world is a bit more complicated than this example suggests: the doctor may be giving the patient the test because other risk factors or symptoms may suggest that the patient is more likely to have the disease than a random member of the population, in which case the above analysis does not apply. Example 8.14. This example is based on the TV game show “Let’s make a deal,” which was popular in the 1970’s. In this game, a contestant chooses one of three doors. Behind two doors is a “zonk,” that is, something amusing but of little or no value, such as a goat, and behind one of the doors is a “grand prize,” such as a car or vacation package. We may assume that the door behind which the grand prize is placed is chosen at random from among the three doors, with equal probability. After the contestant chooses a door, the host of the show, Monty Hall, always reveals a zonk behind one of the two doors not chosen by the contestant. The contestant is then given a choice: either stay with his initial choice of door, or switch to the other unopened door. After the contestant finalizes his decision on which door to choose, that door is opened and he wins whatever is behind it. The question is, which strategy is better for the contestant: to stay or to switch? Let us evaluate the two strategies. If the contestant always stays with his initial selection, then it is clear that his probability of success is exactly 1=3. Now consider the strategy of always switching. Let B be the event that the contestant’s initial choice was correct, and let A be the event that the contestant wins the grand prize. On the one hand, if the contestant’s initial choice was correct, then switching will certainly lead to failure (in this case, Monty has two doors to choose from, but his choice does not affect the outcome). Thus, PŒA j B D 0. On the other hand, suppose that the contestant’s initial choice was incorrect, so that one of the zonks is behind the initially chosen door. Since Monty reveals the x D 1. other zonk, switching will lead with certainty to success. Thus, PŒA j B Furthermore, it is clear that PŒB D 1=3. So using total probability (8.10), we compute x PŒB x D 0 .1=3/ C 1 .2=3/ D 2=3: PŒA D PŒA j BPŒB C PŒA j B Thus, the “stay” strategy has a success probability of 1=3, while the “switch” strategy has a success probability of 2=3. So it is better to switch than to stay.
8.2 Conditional probability and independence
217
Of course, real life is a bit more complicated. Monty did not always reveal a zonk and offer a choice to switch. Indeed, if Monty only revealed a zonk when the contestant had chosen the correct door, then switching would certainly be the wrong strategy. However, if Monty’s choice itself was a random decision made independent of the contestant’s initial choice, then switching is again the preferred strategy. We next generalize the notion of independence from pairs of events to families of events. Let fAi gi 2I be a finite family of events. For a given positive integer k, we say that the family fAi gi 2I is k-wise independent if the following holds: h\ i Y P Aj D PŒAj for all J I with jJ j k: j 2J
j 2J
The family fAi gi 2I is called pairwise independent if it is 2-wise independent. Equivalently, pairwise independence means that for all i; j 2 I with i ¤ j , we have PŒAi \ Aj D PŒAi PŒAj , or put yet another way, that for all i; j 2 I with i ¤ j , the events Ai and Aj are independent. The family fAi gi 2I is called mutually independent if it is k-wise independent for all positive integers k. Equivalently, mutual independence means that h\ i Y PŒAj for all J I : P Aj D j 2J
j 2J
If n WD jI j > 0, mutual independence is equivalent to n-wise independence; moreover, if 0 < k n, then fAi gi 2I is k-wise independent if and only if fAj gj 2J is mutually independent for every J I with jJ j D k. In defining independence, the choice of the index set I plays no real role, and we can rename elements of I as convenient. Example 8.15. Suppose we toss a fair coin three times, which we formally model using the uniform distribution on the set of all 8 possible outcomes of the three coin tosses: (heads, heads, heads), (heads, heads, tails), etc., as in Example 8.8. For i D 1; 2; 3, let Ai be the event that the i th toss comes up heads. Then fAi g3iD1 is a mutually independent family of events, where each individual Ai occurs with probability 1=2. Now let B12 be the event that the first and second tosses agree (i.e., both heads or both tails), let B13 be the event that the first and third tosses agree, and let B23 be the event that the second and third tosses agree. Then the family of events B12 ; B13 ; B23 is pairwise independent, but not mutually independent. Indeed, the probability that any given individual event occurs is 1=2, and the probability that any given pair of events occurs is 1=4; however, the probability that all three events occur is also 1=4, since if any two events occur, then so does the third.
218
Finite and discrete probability distributions
We close this section with some simple facts about independence of events and their complements. x Theorem 8.2. If A and B are independent events, then so are A and B. Proof. We have x (by total probability (8.9)) PŒA D PŒA \ B C PŒA \ B x (since A and B are independent). D PŒAPŒB C PŒA \ B Therefore, x D PŒA PŒA \ B
PŒAPŒB D PŒA.1
x PŒB/ D PŒAPŒB:
This theorem implies that A and B are independent ” A and Bx are independent ” Ax and B " " ” Ax and Bx "
"
:
The following theorem generalizes this result to families of events. It says that if a family of events is k-wise independent, then the family obtained by complementing any number of members of the given family is also k-wise independent. Theorem 8.3. Let fAi gi 2I be a finite, k-wise independent family of events. Let J be a subset of I , and for each i 2 I , define A0i WD Ai if i 2 J , and A0i WD Axi if i … J . Then fA0i gi 2I is also k-wise independent. Proof. It suffices to prove the theorem for the case where J D I n fd g, for an arbitrary d 2 I : this allows us to complement any single member of the family that we wish, without affecting independence; by repeating the procedure, we can complement any number of them. To this end, it will suffice to show the following: if J I , jJ j < k, d 2 I n J , T and AJ WD j 2J Aj , we have Y xd \ AJ D .1 PŒAd / PŒA PŒAj : (8.12) j 2J
Using total probability (8.9), along with the independence hypothesis (twice), we have Y xd \ AJ PŒAj D PŒAJ D PŒAd \ AJ C PŒA j 2J
D PŒAd
Y j 2J
xd \ AJ ; PŒAj C PŒA
8.2 Conditional probability and independence
219
from which (8.12) follows immediately. E XERCISE 8.5. For events A1 ; : : : ; An , define ˛1 WD PŒA1 , and for i D 2; : : : ; n, define ˛i WD PŒAi j A1 \ \ Ai 1 . Show that PŒA1 \ \ An D ˛1 ˛n . E XERCISE 8.6. Let B be an event, and let fBi gi 2I be a finite, pairwise disjoint family of events whose union is B. Generalizing the law of total probability (equations (8.9) and (8.10)), show that every event A, we have PŒA \ B D P i 2I PŒA \ Bi , and if PŒB ¤ 0, then X PŒA j BPŒB D PŒA j Bi PŒBi ; i 2I
where I WD fi 2 I W PŒBi ¤ 0g. Also show that if PŒA j Bi ˛ for each i 2 I , then PŒA j B ˛. E XERCISE 8.7. Three fair coins are tossed. Let A be the event that at least two coins are heads. Let B be the event that the number of heads is odd. Let C be the event that the third coin is heads. Are A and B independent? A and C? B and C? E XERCISE 8.8. Consider again the situation in Example 8.11, but now suppose that Alice only tells Bob the value of the sum of the two dice modulo 6. Describe an optimal strategy for Bob, and calculate his overall probability of winning. E XERCISE 8.9. Consider again the situation in Example 8.13, but now suppose that the patient is visiting the doctor because he has symptom Y . Furthermore, it is known that everyone who has disease X exhibits symptom Y , while 10% of the population overall exhibits symptom Y . Assuming that the accuracy of the test is not affected by the presence of symptom Y , how should the doctor advise his patient should the test come out positive? E XERCISE 8.10. This exercise develops an alternative proof, based on probability theory, of Theorem 2.11. Let n be a positive integer and consider an experiment in which a number a is chosen at random from f0; : : : ; n 1g. If n D p1e1 prer is the prime factorization of n, let Ai be the event that a is divisible by pi , for i D 1; : : : ; r. (a) Show that .n/=n D PŒAx1 \ \ Axr ; where is Euler’s phi function. (b) Show that if J f1; : : : ; rg, then h\ i .Y P Aj D 1 pj : j 2J
fAi griD1
Conclude that for each i D 1; : : : ; r.
j 2J
is mutually independent, and that PŒAi D 1=pi
220
Finite and discrete probability distributions
(c) Using part (b), deduce that x1 \ \ Axr D PŒA
r Y
.1
1=pi /:
i D1
(d) Combine parts (a) and (c) to derive the result of Theorem 2.11 that .n/ D n
r Y
.1
1=pi /:
i D1
8.3 Random variables It is sometimes convenient to associate a real number, or other mathematical object, with each outcome of a random experiment. The notion of a random variable formalizes this idea. Let P be a probability distribution on a sample space ˝. A random variable X is a function X W ˝ ! S , where S is some set, and we say that X takes values in S . We do not require that the values taken by X are real numbers, but if this is the case, we say that X is real valued. For s 2 S , “X D s” denotes the event f! 2 ˝ W X .!/ D sg. It is immediate from this definition that X PŒX D s D P.!/: !2X
1 .fsg/
More generally, for any predicate on S, we may write “ .X /” as shorthand for the event f! 2 ˝ W .X .!//g. When we speak of the image of X , we simply mean its image in the usual function-theoretic sense, that is, the set X .˝/ D fX .!/ W ! 2 ˝g. While a random variable is simply a function on the sample space, any discussion of its properties always takes place relative to a particular probability distribution, which may be implicit from context. One can easily combine random variables to define new random variables. Suppose X1 ; : : : ; Xn are random variables, where Xi W ˝ ! Si for i D 1; : : : ; n. Then .X1 ; : : : ; Xn / denotes the random variable that maps ! 2 ˝ to .X1 .!/; : : : ; Xn .!// 2 S1 Sn . If f W S1 Sn ! T is a function, then f .X1 ; : : : ; Xn / denotes the random variable that maps ! 2 ˝ to f .X1 .!/; : : : ; Xn .!//. If f is applied using a special notation, the same notation may be applied to denote the resulting random variable; for example, if X and Y are random variables taking values in a set S, and ? is a binary operation on S, then X ? Y denotes the random variable that maps ! 2 ˝ to X .!/ ? Y .!/ 2 S. Let X be a random variable whose image is S . The variable X determines a probability distribution PX W S ! Œ0; 1 on the set S, where PX .s/ WD PŒX D s
221
8.3 Random variables
for each s 2 S . We call PX the distribution of X . If PX is the uniform distribution on S, then we say that X is uniformly distributed over S . Example 8.16. Again suppose we roll two dice, and model this experiment as the uniform distribution on ˝ WD f1; : : : ; 6g f1; : : : ; 6g. We can define the random variable X that takes the value of the first die, and the random variable Y that takes the value of the second; formally, X and Y are functions on ˝, where X .s; t / WD s and Y .s; t / WD t for .s; t / 2 ˝: For each value s 2 f1; : : : ; 6g, the event X D s consists of the 6 pairs .s; 1/; : : : ; .s; 6/, and so PŒX D s D 6=36 D 1=6: Thus, X is uniformly distributed over f1; : : : ; 6g. Likewise, Y is uniformly distributed over f1; : : : ; 6g, and the random variable .X ; Y / is uniformly distributed over ˝. We can also define the random variable Z WD X C Y , which formally is the function on the sample space defined by Z .s; t / WD s C t for .s; t/ 2 ˝: The image of Z is f2; : : : ; 12g, and its distribution is given by the following table: u
2
3
4
5
6
7
8
9
10
11
12
PŒZ D u 1/36 2/36 3/36 4/36 5/36 6/36 5/36 4/36 3/36 2/36 1/36
Example 8.17. If A is an event, we may define a random variable X as follows: X WD 1 if the event A occurs, and X WD 0 otherwise. The variable X is called the indicator variable for A. Formally, X is the function that maps ! 2 A to 1, and ! 2 ˝ n A to 0; that is, X is simply the characteristic function of A. The distribution of X is that of a Bernoulli trial: PŒX D 1 D PŒA and PŒX D 0 D 1 PŒA. x Now suppose B is It is not hard to see that 1 X is the indicator variable for A. another event, with indicator variable Y . Then it is also not hard to see that XY is the indicator variable for A \ B, and that X C Y XY is the indicator variable for A [ B; in particular, if A \ B D ;, then X C Y is the indicator variable for A [ B. Example 8.18. Consider again Example 8.8, where we have a coin that comes up heads with probability p, and tails with probability q WD 1 p, and we toss it n times. For each i D 1; : : : ; n, let Ai be the event that the i th toss comes up heads, and let Xi be the corresponding indicator variable. Let us also define X WD X1 C C Xn , which represents the total number of tosses that come up heads. The image of X is f0; : : : ; ng. By the calculations made in Example 8.8, for
222
Finite and discrete probability distributions
each k D 0; : : : ; n, we have ! n k n PŒX D k D p q k
k
:
The distribution of the random variable X is called a binomial distribution. Such a distribution is parameterized by the success probability p of the underlying Bernoulli trial, and by the number of times n the trial is repeated. Uniform distributions are very nice, simple distributions. It is therefore good to have simple criteria that ensure that certain random variables have uniform distributions. The next theorem provides one such criterion. We need a definition: if S and T are finite sets, then we say that a given function f W S ! T is a regular function if every element in the image of f has the same number of pre-images under f . Theorem 8.4. Suppose f W S ! T is a surjective, regular function, and that X is a random variable that is uniformly distributed over S . Then f .X / is uniformly distributed over T . Proof. The assumption that f is surjective and regular implies that for every t 2 T , the set S t WD f 1 .ft g/ has size jS j=jT j. So, for each t 2 T , working directly from the definitions, we have X X X X PŒf .X / D t D P.!/ D P.!/ D PŒX D s !2X
D
X
1 .S
t/
s2S t !2X
1 .fsg/
s2S t
1=jS j D .jS j=jT j/=jS j D 1=jT j:
s2S t
As a corollary, we have: Theorem 8.5. Suppose that W G ! G 0 is a surjective homomorphism of finite abelian groups G and G 0 , and that X is a random variable that is uniformly distributed over G. Then .X / is uniformly distributed over G 0 . Proof. It suffices to show that is regular. Recall that the kernel K of is a subgroup of G, and that for every g 0 2 G 0 , the set 1 .fg 0 g/ is a coset of K (see Theorem 6.19); moreover, every coset of K has the same size (see Theorem 6.14). These facts imply that is regular. Example 8.19. Let us continue with Example 8.16. Recall that for a given integer a, and positive integer n, Œan 2 Zn denotes the residue class of a modulo n. Let us define X 0 WD ŒX 6 and Y 0 WD ŒY 6 . It is not hard to see that both X 0 and Y 0 are uniformly distributed over Z6 , while .X 0 ; Y 0 / is uniformly distributed
8.3 Random variables
223
over Z6 Z6 . Let us define Z 0 WD X 0 C Y 0 (where addition here is in Z6 ). We claim that Z 0 is uniformly distributed over Z6 . This follows immediately from fact that the map that sends .a; b/ 2 Z6 Z6 to a C b 2 Z6 is a surjective group homomorphism (see Example 6.45). Further, we claim that .X 0 ; Z 0 / is uniformly distributed over Z6 Z6 . This follows immediately from fact that the map that sends .a; b/ 2 Z6 Z6 to .a; aCb/ 2 Z6 Z6 is a surjective group homomorphism (indeed, it is a group isomorphism). Let X be a random variable whose image is S . Let B be an event with PŒB ¤ 0. The conditional distribution of X given B is defined to be the distribution of X relative to the conditional distribution P. j B/, that is, the distribution PX ;B W S ! Œ0; 1 defined by PX ;B .s/ WD PŒX D s j B for s 2 S . Suppose X and Y are random variables, with images S and T , respectively. We say X and Y are independent if for all s 2 S and all t 2 T , the events X D s and Y D t are independent, which is to say, PŒ.X D s/ \ .Y D t / D PŒX D sPŒY D t :
Equivalently, X and Y are independent if and only if the distribution of .X ; Y / is essentially equal to the product of the distribution of X and the distribution of Y . As a special case, if X is uniformly distributed over S , and Y is uniformly distributed over T , then X and Y are independent if and only if .X ; Y / is uniformly distributed over S T . Independence can also be characterized in terms of conditional probabilities. From the definitions, it is immediate that X and Y are independent if and only if for all values t taken by Y with non-zero probability, we have PŒX D s j Y D t D PŒX D s
for all s 2 S; that is, the conditional distribution of X given Y D t is the same as the distribution of X . From this point of view, an intuitive interpretation of independence is that information about the value of one random variable does not reveal any information about the value of the other. Example 8.20. Let us continue with Examples 8.16 and 8.19. The random variables X and Y are independent: each is uniformly distributed over f1; : : : ; 6g, and .X ; Y / is uniformly distributed over f1; : : : ; 6g f1; : : : ; 6g. Let us calculate the conditional distribution of X given Z D 4. We have PŒX D s j Z D 4 D 1=3 for s D 1; 2; 3, and PŒX D s j Z D 4 D 0 for s D 4; 5; 6. Thus, the conditional distribution of X given Z D 4 is essentially the uniform distribution on f1; 2; 3g. Let us calculate the conditional distribution of Z given X D 1. We have PŒZ D u j X D 1 D 1=6 for u D 2; : : : ; 7, and PŒZ D u j X D 1 D 0 for u D 8; : : : ; 12. Thus, the conditional distribution of Z given X D 1 is essentially
224
Finite and discrete probability distributions
the uniform distribution on f2; : : : ; 7g. In particular, it is clear that X and Z are not independent. The random variables X 0 and Y 0 are independent, as are X 0 and Z 0 : each of X 0 , Y 0 , and Z 0 is uniformly distributed over Z6 , and each of .X 0 ; Y 0 / and .X 0 ; Z 0 / is uniformly distributed over Z6 Z6 . We now generalize the notion of independence to families of random variables. Let fXi gi 2I be a finite family of random variables. Let us call a corresponding family of values fsi gi 2I an assignment to fXi gi 2I if si is in the image of Xi for each i 2 I . For a given positive integer k, we say that the family fXi gi2I is kwise independent if for every assignment fsi gi 2I to fXi gi 2I , the family of events fXi D si gi 2I is k-wise independent. The notions of pairwise and mutual independence for random variables are defined following the same pattern that was used for events. The family fXi gi 2I is called pairwise independent if it is 2-wise independent, which means that for all i; j 2 I with i ¤ j , the variables Xi and Xj are independent. The family fXi gi 2I is called mutually independent if it is k-wise independent for all positive integers k. Equivalently, and more explicitly, mutual independence means that for every assignment fsi gi 2I to fXi gi 2I , we have h\ i Y P .Xj D sj / D PŒXj D sj for all J I : (8.13) j 2J
j 2J
If n WD jI j > 0, mutual independence is equivalent to n-wise independence; moreover, if 0 < k n, then fXi gi 2I is k-wise independent if and only if fXj gj 2J is mutually independent for every J I with jJ j D k. Example 8.21. Returning again to Examples 8.16, 8.19, and 8.20, we see that the family of random variables X 0 ; Y 0 ; Z 0 is pairwise independent, but not mutually independent; for example, P .X 0 D Œ06 / \ .Y 0 D Œ06 / \ .Z 0 D Œ06 / D 1=62 ; but P X 0 D Œ06 P Y 0 D Œ06 P Z 0 D Œ06 D 1=63 :
Example 8.22. Suppose fAi gi 2I is a finite family of events. Let fXi gi 2I be the corresponding family of indicator variables, so that for each i 2 I , Xi D 1 if Ai occurs, and Xi D 0, otherwise. Theorem 8.3 immediately implies that for every positive integer k, fAi gi 2I is k-wise independent if and only if fXi gi 2I is k-wise independent. Example 8.23. Consider again Example 8.15, where we toss a fair coin 3 times. For i D 1; 2; 3, let Xi be the indicator variable for the event Ai that the i th toss
225
8.3 Random variables
comes up heads. Then fXi g3iD1 is a mutually independent family of random variables. Let Y12 be the indicator variable for the event B12 that tosses 1 and 2 agree; similarly, let Y13 be the indicator variable for the event B13 , and Y23 the indicator variable for B23 . Then the family of random variables Y12 ; Y13 ; Y23 is pairwise independent, but not mutually independent. We next present a number of useful tools for establishing independence. Theorem 8.6. Let X be a random variable with image S, and Y be a random variable with image T . Further, suppose that f W S ! Œ0; 1 and g W T ! Œ0; 1 are functions such that X X f .s/ D g.t / D 1; (8.14) s2S
t 2T
and that for all s 2 S and t 2 T , we have PŒ.X D s/ \ .Y D t / D f .s/g.t /:
(8.15)
Then X and Y are independent, the distribution of X is f , and the distribution of Y is g. Proof. Since fY D tg t 2T is a partition of the sample space, making use of total probability (8.9), along with (8.15) and (8.14), we see that for all s 2 S, we have X X X g.t / D f .s/: f .s/g.t / D f .s/ PŒ.X D s/ \ .Y D t / D PŒX D s D t 2T
t 2T
t 2T
Thus, the distribution of X is indeed f . Exchanging the roles of X and Y in the above argument, we see that the distribution of Y is g. Combining this with (8.15), we see that X and Y are independent. The generalization of Theorem 8.6 to families of random variables is a bit messy, but the basic idea is the same: Theorem 8.7. Let fXi gi 2I be a finite family of random variables, where each Xi has image Si . Also, let ffi gi 2I be a family of functions, where for each i 2 I , P fi W Si ! Œ0; 1 and si 2Si fi .si / D 1. Further, suppose that h\ i Y P .Xi D si / D fi .si / i 2I
i 2I
for each assignment fsi gi 2I to fXi gi 2I . Then the family fXi gi 2I is mutually independent, and for each i 2 I , the distribution of Xi is fi . Proof. To prove the theorem, it suffices to prove the following statement: for every
226
Finite and discrete probability distributions
subset J of I , and every assignment fsj gj 2J to fXj gj 2J , we have i Y h\ .Xj D sj / D fj .sj /: P j 2J
j 2J
Moreover, it suffices to prove this statement for the case where J D I n fd g, for an arbitrary d 2 I : this allows us to eliminate any one variable from the family, without affecting the hypotheses, and by repeating this procedure, we can eliminate any number of variables. Thus, let d 2 I be fixed, let J WD I n fd g, and let fsj gj 2J be a fixed assignment to fXj gj 2J . Then, since fXd D sd gsd 2Sd is a partition of the sample space, we have i h\ i h [ \ i X h\ P .Xi D si / P .Xj D sj / D P .Xi D si / D j 2J
D
sd 2Sd
sd 2Sd i 2I
X Y
fi .si / D
Y
fj .sj /
j 2J
sd 2Sd i 2I
X sd 2Sd
fd .sd / D
Y
i 2I
fj .sj /:
j 2J
This theorem has several immediate consequences. First of all, mutual independence may be more simply characterized: Theorem 8.8. Let fXi gi 2I be a finite family of random variables. Suppose that for every assignment fsi gi 2I to fXi gi 2I , we have h\ i Y P .Xi D si / D PŒXi D si : i 2I
i 2I
Then fXi gi 2I is mutually independent. Theorem 8.8 says that to check for mutual independence, we only have to consider the index set J D I in (8.13). Put another way, it says that a family of random variables fXi gniD1 is mutually independent if and only if the distribution of .X1 ; : : : ; Xn / is essentially equal to the product of the distributions of the individual Xi ’s. Based on the definition of mutual independence, and its characterization in Theorem 8.8, the following is also immediate: Theorem 8.9. Suppose fXi gniD1 is a family of random variables, and that m is an integer with 0 < m < n. Then the following are equivalent: (i) fXi gniD1 is mutually independent; n (ii) fXi gm i D1 is mutually independent, fXi gi DmC1 is mutually independent, and the two variables .X1 ; : : : ; Xm / and .XmC1 ; : : : ; Xn / are independent. The following is also an immediate consequence of Theorem 8.7 (it also follows easily from Theorem 8.4).
227
8.3 Random variables
Theorem 8.10. Suppose that X1 ; : : : ; Xn are random variables, and that S1 ; : : : ; Sn are finite sets. Then the following are equivalent: (i) .X1 ; : : : ; Xn / is uniformly distributed over S1 Sn ; (ii) fXi gniD1 is mutually independent, with each Xi uniformly distributed over Si . Another immediate consequence of Theorem 8.7 is the following: Theorem 8.11. Suppose P is the product distribution P1 Pn , where each Pi is a probability distribution on a sample space ˝i , so that the sample space of P is ˝ D ˝1 ˝n . For each i D 1; : : : ; n, let Xi be the random variable that projects on the i th coordinate, so that Xi .!1 ; : : : ; !n / D !i . Then fXi gniD1 is mutually independent, and for each i D 1; : : : ; n, the distribution of Xi is Pi . Theorem 8.11 is often used to synthesize independent random variables “out of thin air,” by taking the product of appropriate probability distributions. Other arguments may then be used to prove the independence of variables derived from these. Example 8.24. Theorem 8.11 immediately implies that in Example 8.18, the family of indicator variables X1 ; : : : ; Xn is mutually independent, where for each i D 1; : : : ; n, PŒXi D 1 D p. The following theorem gives us yet another way to establish independence. Theorem 8.12. Suppose fXi gniD1 is a mutually independent family of random variables. Further, suppose that for i D 1; : : : ; n, Yi WD gi .Xi / for some function gi . Then fYi gniD1 is mutually independent. Proof. It suffices to prove the theorem for n D 2. The general case follows easily by induction, using Theorem 8.9. For i D 1; 2, let ti be any value in the image of Yi , and let Si0 WD gi 1 .fti g/. We have h [ [ i PŒ.Y1 D t1 / \ .Y2 D t2 / D P .X1 D s1 / \ .X2 D s2 / s1 2S10
h [
DP
s2 2S20
[
i .X1 D s1 / \ .X2 D s2 /
s1 2S10 s2 2S20
D
X X s1 2S10
D
PŒ.X1 D s1 / \ .X2 D s2 /
s2 2S20
X X s1 2S10 s2 2S20
PŒX1 D s1 PŒX2 D s2
228
Finite and discrete probability distributions
D
X
PŒX1 D s1
X
PŒX2 D s2
s1 2S10
s2 2S20
h [
i i h [ .X2 D s2 / .X1 D s1 / P
DP
s1 2S10
s2 2S20
D PŒY1 D t1 PŒY2 D t2 : As a special case of the above theorem, if each gi is the characteristic function for some subset Si0 of the image of Xi , then X1 2 S10 ; : : : ; Xn 2 Sn0 form a mutually independent family of events. The next theorem is quite handy in proving the independence of random variables in a variety of algebraic settings. Theorem 8.13. Suppose that G be a finite abelian group, and that W is a random variable uniformly distributed over G. Let Z be another random variable, taking values in some finite set U , and suppose that W and Z are independent. Let W U ! G be some function, and define Y WD W C .Z /. Then Y is uniformly distributed over G, and Y and Z are independent. Proof. Consider any fixed values t 2 G and u 2 U . Evidently, the events .Y D t / \ .Z D u/ and .W D t .u// \ .Z D u/ are the same, and therefore, because W and Z are independent, we have PŒ.Y D t/ \ .Z D u/ D PŒW D t
.u/PŒZ D u D
1 PŒZ D u: (8.16) jGj
Since this holds for every u 2 U , making use of total probability (8.9), we have X 1 1 X : PŒY D t D PŒ.Y D t / \ .Z D u/ D PŒZ D u D jGj jGj u2U
u2U
Thus, Y is uniformly distributed over G, and by (8.16), Y and Z are independent. (This conclusion could also have been deduced directly from (8.16) using Theorem 8.6—we have repeated the argument here.) Note that in the above theorem, we make no assumption about the distribution of Z , or any properties of the function . Example 8.25. Theorem 8.13 may be used to justify the security of the one-time pad encryption scheme. Here, the variable W represents a random, secret key — the “pad”—that is shared between Alice and Bob; U represents a space of possible messages; Z represents a “message source,” from which Alice draws her message according to some distribution; finally, the function W U ! G represents some invertible “encoding transformation” that maps messages into group elements.
8.3 Random variables
229
To encrypt a message drawn from the message source, Alice encodes the message as a group element, and then adds the pad. The variable Y WD W C .Z / represents the resulting ciphertext. Since Z D 1 .Y W /, when Bob receives the ciphertext, he decrypts it by subtracting the pad, and converting the resulting group element back into a message. Because the message source Z and ciphertext Y are independent, an eavesdropping adversary who learns the value of Y does not learn anything about Alice’s message: for any particular ciphertext t , the conditional distribution of Z given Y D t is the same as the distribution of Z . The term “one time” comes the fact that a given encryption key should be used only once; otherwise, security may be compromised. Indeed, suppose the key is used a second time, encrypting a message drawn from a second source Z 0 . The second ciphertext is represented by the random variable Y 0 WD W C .Z 0 /. In general, the random variables .Z ; Z 0 / and .Y ; Y 0 / will not be independent, since Y Y 0 D .Z / .Z 0 /. To illustrate this more concretely, suppose Z is uniformly distributed over a set of 1000 messages, Z 0 is uniformly distributed over a set of two messages, say, fu01 ; u02 g, and that Z and Z 0 are independent. Now, without any further information about Z , and adversary would have at best a 1-in-a-1000 chance of guessing its value. However, if he sees that Y D t and Y 0 D t 0 , for particular values t; t 0 2 G, then he has a 1-in-2-chance, since the value of Z is equally likely to be one of just two messages, namely, u1 WD 1 .t t 0 C .u01 // and u2 WD 1 .t t 0 C .u02 //; more formally, the conditional distribution of Z given .Y D t/ \ .Y 0 D t 0 / is essentially the uniform distribution on fu1 ; u2 g. In practice, it is convenient to define the group G to be the group of all bit strings of some fixed length, with bit-wise exclusive-or as the group operation. The encoding function simply “serializes” a message as a bit string. Example 8.26. Theorem 8.13 may also be used to justify a very simple type of secret sharing. A colorful, if militaristic, motivating scenario is the following. To launch a nuclear missile, two officers who carry special keys must insert their keys simultaneously into the “authorization device” (at least, that is how it works in Hollywood). In the digital version of this scenario, an authorization device contains a secret, digital “launch code,” and each officer holds a digital “share” of this code, so that (i) individually, each share reveals no information about the launch code, but (ii) collectively, the two shares may be combined in a simple way to derive the launch code. Thus, to launch the missile, both officers must input their shares into the authorization device; hardware in the authorization device combines the two shares, and compares the resulting code against the launch code it stores — if they match, the missile flies. In the language of Theorem 8.13, the launch code is represented by the random variable Z , and the two shares by W and Y WD W C .Z /, where (as in the previ-
230
Finite and discrete probability distributions
ous example) W U ! G is some simple, invertible encoding function. Because W and Z are independent, information about the share W leaks no information about the launch code Z ; likewise, since Y and Z are independent, information, about Y leaks no information about Z . However, by combining both shares, the launch code is easily constructed as Z D 1 .Y W /. Example 8.27. Let k be a positive integer. This example shows how we can take a mutually independent family of k random variables, and from it, construct a much larger, k-wise independent family of random variables. Let p be a prime, with p k. Let fHi gikD01 be a mutually independent family of random variables, each of which is uniformly distributed over Zp . Let us set H WD .H0 ; : : : ; Hk 1 /, which by assumption, is uniformly distributed over Zpk . For each s 2 Zp , we define the function s W Zpk ! Zp as follows: for r D P .r0 ; : : : ; rk 1 / 2 Zpk , s .r/ WD ki D01 ri s i ; that is, s .r/ is the value obtained by evaluating the polynomial r0 C r1 X C C rk 1 Xk 1 2 Zp ŒX at the point s. Each s 2 Zp defines a random variable s .H/ D H0 C H1 s C C Hk 1 s k 1 . We claim that the family of random variables fs .H/gs2Zp is k-wise independent, with each individual s .H/ uniformly distributed over Zp . By Theorem 8.10, it suffices to show the following: for all distinct points s1 ; : : : ; sk 2 Zp , the random variable W WD .s1 .H/; : : : ; sk .H// is uniformly distributed over Zpk . So let s1 ; : : : ; sk be fixed, distinct elements of Zp , and define the function W Zpk ! Zpk r 7! .s1 .r/; : : : ; sk .r//:
(8.17)
Thus, W D .H/, and by Lagrange interpolation (Theorem 7.15), the function is a bijection; moreover, since H is uniformly distributed over Zpk , so is W . Of course, the field Zp may be replaced by an arbitrary finite field. Example 8.28. Consider again the secret sharing scenario of Example 8.26. Suppose at the critical moment, one of the officers is missing in action. The military planners would perhaps like a more flexible secret sharing scheme; for example, perhaps shares of the launch code should be distributed to three officers, in such a way that no single officer can authorize a launch, but any two can. More generally, for positive integers k and `, with ` k C 1, the scheme should distribute shares among ` officers, so that no coalition of k (or fewer) officers can authorize a launch, yet any coalition of k C 1 officers can. Using the construction of the previous example, this is easily achieved, as follows. Let us model the secret launch code as a random variable Z , taking values in a finite set U . Assume that p is prime, with p k C 1, and that W U ! Zp is a simple, invertible encoding function. To construct the shares, we make use of
231
8.3 Random variables
random variables H0 ; : : : ; Hk 1 , where each Hi is uniformly distributed over Zp , and the family of random variables H0 ; : : : ; Hk 1 ; Z is mutually independent. For each s 2 Zp , we define the random variable Ys WD H0 C H1 s C C Hk
1s
k 1
C .Z /s k :
We can pick any subset S Zp of size ` that we wish, so that for each s 2 S , an officer gets the secret share Ys (along with the public value s). First, we show how any coalition of k C 1 officers can reconstruct the launch code from their collection of shares, say, Ys1 ; : : : ; YskC1 . This is easily done by means of the Lagrange interpolation formula (again, Theorem 7.15). Indeed, we only need to recover the high-order coefficient, .Z /, which we can obtain via the formula kC1 X Ysi Q .Z / D : sj / j ¤i .si i D1
Second, we show that no coalition of k officers learn anything about the launch code, even if they pool their shares. Formally, this means that if s1 ; : : : ; sk are fixed, distinct points, then Ys1 ; : : : ; Ysk ; Z form a mutually independent family of random variables. This is easily seen, as follows. Define H WD .H0 ; : : : ; Hk 1 /, and W WD .H/, where W Zpk ! Zpk is as defined in (8.17), and set Y WD .Ys1 ; : : : ; Ysk /. Now, by hypothesis, H and Z are independent, and H is uniformly distributed over Zpk . As we noted in Example 8.27, is a bijection, and hence, W is uniformly distributed over Zpk ; moreover (by Theorem 8.12), W and Z are independent. Observe that Y D W C 0 .Z /, where 0 maps u 2 U to . .u/s1k ; : : : ; .u/skk / 2 Zpk , and so applying Theorem 8.13 (with the group Zpk , the random variables W and Z , and the function 0 ), we see that Y and Z are independent, where Y is uniformly distributed over Zpk . From this, it follows (using Theorems 8.9 and Theorem 8.10) that the family of random variables Ys1 ; : : : ; Ysk ; Z is mutually independent, with each Ysi uniformly distributed over Zp . Finally, we note that when k D 1, ` D 2, and S D f0; 1g, this construction degenerates to the construction in Example 8.26. E XERCISE 8.11. Suppose X and X 0 are random variables that take values in a set S and that have essentially the same distribution. Show that if f W S ! T is a function, then f .X / and f .X 0 / have essentially the same distribution. E XERCISE 8.12. Let fXi gniD1 be a family of random variables, and let Si be the image of Xi for i D 1; : : : ; n. Show that fXi gniD1 is mutually independent if and
232
Finite and discrete probability distributions
only if for each i D 2; : : : ; n, and for all s1 2 S1 ; : : : ; si 2 Si , we have PŒXi D si j .X1 D s1 / \ \ .Xi
1
D si
1 /
D PŒXi D si :
E XERCISE 8.13. Suppose that W G ! G 0 is a surjective group homomorphism, where G and G 0 are finite abelian groups. Show that if g 0 ; h0 2 G 0 , and X and Y are independent random variables, where X is uniformly distributed over 1 .fg 0 g/, and Y takes values in 1 .fh0 g/, then X C Y is uniformly distributed over 1 .fg 0 C h0 g/. E XERCISE 8.14. Suppose X and Y are random variables, where X takes values in S , and Y takes values in T . Further suppose that Y 0 is uniformly distributed over T , and that .X ; Y / and Y 0 are independent. Let be a predicate on S T . Show that PŒ .X ; Y / \ .Y D Y 0 / D PŒ .X ; Y /=jT j. E XERCISE 8.15. Let X and Y be independent random variables, where X is uniformly distributed a set S , and Y is uniformly distributed over a set T S. Define a third random variable Z as follows: if X 2 T , then Z WD X ; otherwise, Z WD Y . Show that Z is uniformly distributed over T . E XERCISE 8.16. Let n be a positive integer, and let X be a random variable, uniformly distributed over f0; : : : ; n 1g. For each positive divisor d of n, let us define the random variable Xd WD X mod d . Show that: (a) if d is a divisor of n, then the variable Xd is uniformly distributed over f0; : : : ; d 1g; (b) if d1 ; : : : ; dk are divisors of n, then fXdi gkiD1 is mutually independent if and only if fdi gkiD1 is pairwise relatively prime. E XERCISE 8.17. Suppose X and Y are random variables, each uniformly distributed over Z2 , but not necessarily independent. Show that the distribution of .X ; Y / is the same as the distribution of .X C 1; Y C 1/. E XERCISE 8.18. Let I WD f1; : : : ; ng, where n 2, let B WD f0; 1g, and let G be a finite abelian group, with jGj > 1. Suppose that fXi b g.i;b/2I B is a mutually independent family of random variables, each uniformly distributed over G. For each ˇ D .b1 ; : : : ; bn / 2 B n , let us define the random variable Yˇ WD X1b1 C C Xnbn . Show that each Yˇ uniformly distributed over G, and that fYˇ gˇ 2B n is 3-wise independent, but not 4-wise independent.
8.4 Expectation and variance
233
8.4 Expectation and variance Let P be a probability distribution on a sample space ˝. If X is a real-valued random variable, then its expected value, or expectation, is X EŒX WD X .!/ P.!/: (8.18) !2˝
If S is the image of X , and if for each s 2 S we group together the terms in (8.18) with X .!/ D s, then we see that X s PŒX D s: (8.19) EŒX D s2S
From (8.19), it is clear that EŒX depends only on the distribution of X : if X 0 is another random variable with the same (or essentially the same) distribution as X , then EŒX D EŒX 0 . More generally, suppose X is an arbitrary random variable (not necessarily real valued) whose image is S , and f is a real-valued function on S . Then again, if for each s 2 S we group together the terms in (8.18) with X .!/ D s, we see that X f .s/PŒX D s: (8.20) EŒf .X / D s2S
We make a few trivial observations about expectation, which the reader may easily verify. First, if X is equal to a constant c (i.e., X .!/ D c for every ! 2 ˝), then EŒX D EŒc D c. Second, if X and Y are random variables such that X Y (i.e., X .!/ Y .!/ for every ! 2 ˝), then EŒX EŒY . Similarly, if X > Y , then EŒX > EŒY . In calculating expectations, one rarely makes direct use of (8.18), (8.19), or (8.20), except in rather trivial situations. The next two theorems develop tools that are often quite effective in calculating expectations. Theorem 8.14 (Linearity of expectation). If X and Y are real-valued random variables, and a is a real number, then EŒX C Y D EŒX C EŒY and EŒaX D aEŒX :
Proof. It is easiest to prove this using the defining equation (8.18) for expectation. For ! 2 ˝, the value of the random variable X C Y at ! is by definition X .!/ C
234
Finite and discrete probability distributions
Y .!/, and so we have EŒX C Y D
X
.X .!/ C Y .!//P.!/
!
D
X
X .!/P.!/ C
X
!
Y .!/P.!/
!
D EŒX C EŒY : For the second part of the theorem, by a similar calculation, we have X X EŒaX D .aX .!//P.!/ D a X .!/P.!/ D aEŒX : !
!
More generally, the above theorem implies (using a simple induction argument) that if fXi gi 2I is a finite family of real-valued random variables, then we have hX i X EŒXi : (8.21) E Xi D i 2I
i 2I
So we see that expectation is linear; however, expectation is not in general multiplicative, except in the case of independent random variables: Theorem 8.15. If X and Y are independent, real-valued random variables, then EŒXY D EŒX EŒY :
Proof. It is easiest to prove this using (8.20), with the function f .s; t / WD st applied to the random variable .X ; Y /. We have X EŒXY D st PŒ.X D s/ \ .Y D t / s;t
D
X
st PŒX D sPŒY D t
s;t
D
X
s PŒX D s
X
s
t PŒY D t
t
D EŒX EŒY :
More generally, the above theorem implies (using a simple induction argument) that if fXi gi 2I is a finite, mutually independent family of real-valued random variables, then hY i Y E Xi D EŒXi : (8.22) i 2I
i 2I
The following simple facts are also sometimes quite useful in calculating expectations:
235
8.4 Expectation and variance
Theorem 8.16. Let X be a 0=1-valued random variable. Then EŒX D PŒX D 1. Proof. EŒX D 0 PŒX D 0 C 1 PŒX D 1 D PŒX D 1. Theorem 8.17. If X is a random variable that takes only non-negative integer values, then X EŒX D PŒX i : i 1
Note that since X has a finite image, the sum appearing above is finite. Proof. Suppose that the image of X is contained in f0; : : : ; ng, and for i D 1; : : : ; n, let Xi be the indicator variable for the event X i . Then X D X1 C C Xn , and by linearity of expectation and Theorem 8.16, we have EŒX D
n X
EŒXi D
i D1
n X
PŒX i :
i D1
Let X be a real-valued random variable with WD EŒX . The variance of X is VarŒX WD EŒ.X /2 . The variance provides a measure of the spread or dispersion of the distribution of X around its expected value. Note that since .X /2 takes only non-negative values, variance is always non-negative. Theorem 8.18. Let X be a real-valued random variable, with WD EŒX , and let a and b be real numbers. Then we have (i) VarŒX D EŒX 2
2 ,
(ii) VarŒaX D a2 VarŒX , and (iii) VarŒX C b D VarŒX . Proof. For part (i), observe that VarŒX D EŒ.X
/2 D EŒX 2
2X C 2
D EŒX 2
2EŒX C EŒ2 D EŒX 2
D EŒX 2
2 ;
22 C 2
where in the third equality, we used the fact that expectation is linear, and in the fourth equality, we used the fact that EŒc D c for constant c (in this case, c D 2 ). For part (ii), observe that VarŒaX D EŒa2 X 2
D a2 .EŒX 2
EŒaX 2 D a2 EŒX 2
.a/2
2 / D a2 VarŒX ;
where we used part (i) in the first and fourth equality, and the linearity of expectation in the second.
236
Finite and discrete probability distributions
Part (iii) follows by a similar calculation (verify): VarŒX C b D EŒ.X C b/2
. C b/2
D .EŒX 2 C 2b C b 2 / D EŒX 2
.2 C 2b C b 2 /
2 D VarŒX :
The following is an immediate consequence of part (i) of Theorem 8.18, and the fact that variance is always non-negative: Theorem 8.19. If X is a real-valued random variable, then EŒX 2 EŒX 2 . Unlike expectation, the variance of a sum of random variables is not equal to the sum of the variances, unless the variables are pairwise independent: Theorem 8.20. If fXi gi 2I is a finite, pairwise independent family of real-valued random variables, then hX i X Var Xi D VarŒXi : i 2I
Proof. We have hX i hX 2 i Var Xi D E Xi i 2I
i 2I
X 2 Xi EΠi 2I
i 2I
D
X
EŒXi2
i 2I
C
X
EŒXi Xj
EŒXi EŒXj
i;j 2I i ¤j
X
EŒXi 2
i 2I
(by linearity of expectation and rearranging terms) X X D EŒXi2 EŒXi 2 i 2I
i 2I
(by pairwise independence and Theorem 8.15) X D VarŒXi : i 2I
Corresponding to Theorem 8.16, we have: Theorem 8.21. Let X be a 0=1-valued random variable, with p WD PŒX D 1 and q WD PŒX D 0 D 1 p. Then VarŒX D pq. Proof. We have EŒX D p and EŒX 2 D PŒX 2 D 1 D PŒX D 1 D p. Therefore, VarŒX D EŒX 2
EŒX 2 D p
p 2 D p.1
p/ D pq:
Let B be an event with PŒB ¤ 0, and let X be a real-valued random variable.
237
8.4 Expectation and variance
We define the conditional expectation of X given B, denoted EŒX j B, to be the expected value of the X relative to the conditional distribution P. j B/, so that X X EŒX j B D X .!/P.! j B/ D PŒB 1 X .!/P.!/: !2B
!2˝
Analogous to (8.19), if S is the image of X , we have X s PŒX D s j B: EŒX j B D
(8.23)
s2S
Furthermore, suppose I is a finite index set, and fBi gi 2I is a partition of the sample space, where each Bi occurs with non-zero probability. If for each i 2 I we group together the terms in (8.18) with ! 2 Bi , we obtain a law of total probability for expectation: X EŒX j Bi PŒBi : (8.24) EŒX D i 2I
Example 8.29. Let X be uniformly distributed over f1; : : : ; mg. Let us compute EŒX and VarŒX . We have EŒX D
m X sD1
s
1 m.m C 1/ 1 mC1 D D : m 2 m 2
We also have 2
EŒX D
m X sD1
s2
1 m.m C 1/.2m C 1/ 1 .m C 1/.2m C 1/ D D : m 6 m 6
Therefore, VarŒX D EŒX 2
EŒX 2 D
m2 1 : 12
Example 8.30. Let X denote the value of a roll of a die. Let A be the event that X is even. Then the conditional distribution of X given A is essentially the uniform distribution on f2; 4; 6g, and hence 2C4C6 D 4: 3 Similarly, the conditional distribution of X given Ax is essentially the uniform disEŒX j A D
tribution on f1; 3; 5g, and so x D EŒX j A
1C3C5 D 3: 3
238
Finite and discrete probability distributions
We can compute the expected value of X using these conditional expectations; indeed, we have 1 7 1 C3 D ; 2 2 2 which agrees with the calculation in previous example. x PŒA x D4 EŒX D EŒX j APŒA C EŒX j A
Example 8.31. Suppose that fXi gniD1 is a mutually independent family of random variables, where each Xi takes the value 1 with probability p and 0 with probability q WD 1 p. Let us set X WD X1 C C Xn . Note that the distribution of each Xi is that of a Bernoulli trial, as in Example 8.3, and the distribution of X is a binomial distribution, as in Example 8.18. By Theorems 8.16 and 8.21, we have EŒXi D p and VarŒXi D pq for i D 1; : : : ; n. Let us compute EŒX and VarŒX . By linearity of expectation, we have n X EŒX D EŒXi D np; i D1
and by Theorem 8.20, and the fact that fXi gniD1 is mutually independent (see Example 8.24), we have VarŒX D
n X
VarŒXi D npq:
i D1
Example 8.32. Our proof of Theorem 8.1 could be elegantly recast in terms of indicator variables. For B ˝, let XB be the indicator variable for B, so that XB .!/ D ı! ŒB for each ! 2 ˝. Equation (8.8) then becomes X XA D . 1/jJ j 1 XAJ ; ;¨J I
and by Theorem 8.16 and linearity of expectation, we have X X PŒA D EŒA D . 1/jJ j 1 EŒXAJ D . 1/jJ j ;¨J I
1
PŒXAJ :
;¨J I
E XERCISE 8.19. Let B be an event, with PŒB ¤ 0, and let fBi gi 2I be a finite, pairwise disjoint family of events whose union is B. Generalizing (8.24), show that for every real-valued random variable X , we have X EŒX j BPŒB D EŒX j Bi PŒBi ; i 2I
where I WD fi 2 I W PŒBi ¤ 0g. Also show that if EŒX j Bi ˛ for each i 2 I , then EŒX j B ˛.
8.4 Expectation and variance
239
E XERCISE 8.20. This exercise makes use of the notion of convexity (see §A8). (a) Prove Jensen’s inequality: if f is convex on an interval, and X is a random variable taking values in that interval, then EŒf .X / f .EŒX /. Hint: use induction on the size of the image of X . (Note that Theorem 8.19 is a special case of this, with f .s/ WD s 2 .) (b) Using part (a), show that if X takes non-negative real values, and ˛ is a positive number, then EŒX ˛ EŒX ˛ if ˛ 1, and EŒX ˛ EŒX ˛ if ˛ 1. (c) Using part (a), show that if X takes positive real values, then EŒX e EŒlog X : (d) Using part (c), derive the arithmetic/geometric mean inequality: for positive numbers x1 ; : : : ; xn , we have .x1 C C xn /=n .x1 xn /1=n : E XERCISE 8.21. Show that if fXi gniD1 is a pairwise independent family of 0/1valued random variables, and X WD X1 C C Xn , then VarŒX EŒX and VarŒX n=4. E XERCISE 8.22. For real-valued random variables X and Y , their covariance is defined as CovŒX ; Y WD EŒXY EŒX EŒY . Show that: (a) if X , Y , and Z are real-valued random variables, and a is a real number, then CovŒX C Y ; Z D CovŒX ; Z C CovŒY ; Z and CovŒaX ; Z D aCovŒX ; Z ; (b) if fXi gi 2I is a finite family of real-valued random variables, then hX i X X Var Xi D VarŒXi C CovŒXi ; Xj : i 2I
i 2I
i;j 2I i ¤j
E XERCISE 8.23. Consider again the game played between Alice and Bob in Example 8.11. Suppose that to play the game, Bob must place a one dollar bet. However, after Alice reveals the sum of the two dice, Bob may elect to double his bet. If Bob’s guess is correct, Alice pays him his bet, and otherwise, Bob pays Alice his bet. Describe an optimal playing strategy for Bob, and calculate his expected winnings. E XERCISE 8.24. A die is rolled repeatedly until it comes up “1,” or until it is rolled n times (whichever comes first). What is the expected number of rolls of the die?
240
Finite and discrete probability distributions
8.5 Some useful bounds In this section, we present several theorems that can be used to bound the probability that a random variable deviates from its expected value by some specified amount. Theorem 8.22 (Markov’s inequality). Let X be a random variable that takes only non-negative real values. Then for every ˛ > 0, we have PŒX ˛ EŒX =˛:
Proof. We have EŒX D
X
s PŒX D s D
s
X
s PŒX D s C
s<˛
X
s PŒX D s;
s˛
where the summations are over elements s in the image of X . Since X takes only non-negative values, all of the terms are non-negative. Therefore, X X EŒX s PŒX D s ˛ PŒX D s D ˛ PŒX ˛: s˛
s˛
Markov’s inequality may be the only game in town when nothing more about the distribution of X is known besides its expected value. However, if the variance of X is also known, then one can get a better bound. Theorem 8.23 (Chebyshev’s inequality). Let X be a real-valued random variable, with WD EŒX and WD VarŒX . Then for every ˛ > 0, we have PŒjX
j ˛ =˛ 2 :
Proof. Let Y WD .X /2 . Then Y is always non-negative, and EŒY D . Applying Markov’s inequality to Y , we have PŒjX
j ˛ D PŒY ˛ 2 =˛ 2 :
An important special case of Chebyshev’s inequality is the following. Suppose that fXi gi 2I is a finite, non-empty, pairwise independent family of real-valued random variables, each with the same distribution. Let be the common value of EŒXi , be the common value of VarŒXi , and n WD jI j. Set X x WD 1 Xi : X n i 2I
x is called the sample mean of fXi gi2I . By the linearity of expecThe variable X x D , and since fXi gi 2I is pairwise independent, it follows tation, we have EŒX
8.5 Some useful bounds
241
x D =n. from Theorem 8.20 (along with part (ii) of Theorem 8.18) that VarŒX Applying Chebyshev’s inequality, for every > 0, we have x j : PŒjX (8.25) n 2 The inequality (8.25) says that for all > 0, and for all ı > 0, there exists n0 (depending on and ı, as well as the variance ) such that n n0 implies x PŒjX
j ı:
(8.26)
In words: As n gets large, the sample mean closely approximates the expected value with high probability. This fact, known as the law of large numbers, justifies the usual intuitive interpretation given to expectation. Let us now examine an even more specialized case of the above situation, and assume that each Xi is a 0=1-valued random variable, taking the value 1 with probability p, and 0 with probability q WD 1 p. By Theorems 8.16 and 8.21, the Xi ’s have a common expected value p and variance pq. Therefore, by (8.25), for every > 0, we have x pj pq : PŒjX (8.27) n 2 The bound on the right-hand side of (8.27) decreases linearly in n. If one makes the stronger assumption that the family fXi gi 2I is mutually independent (so that P X WD i Xi has a binomial distribution), one can obtain a much better bound that decreases exponentially in n: Theorem 8.24 (Chernoff bound). Let fXi gi 2I be a finite, non-empty, and mutually independent family of random variables, such that each Xi is 1 with probability p and 0 with probability q WD 1 p. Assume that 0 < p < 1. Also, let n WD jI j x be the sample mean of fXi gi 2I . Then for every > 0, we have: and X x p e n2 =2q I (i) PŒX x (ii) PŒX x (iii) PŒjX
p
e
pj 2e
n 2 =2p n 2 =2
I
:
Proof. First, we observe that (ii) follows directly from (i) by replacing Xi by 1 Xi and exchanging the roles of p and q. Second, we observe that (iii) follows directly from (i) and (ii). Thus, it suffices to prove (i). Let ˛ > 0 be a parameter, whose value will be determined later. Define the ranx dom variable Z WD e ˛ n.X p/ . Since the function x 7! e ˛ nx is strictly increasing,
242
x we have X that
Finite and discrete probability distributions
p if and only if Z e ˛ n . By Markov’s inequality, it follows p D PŒZ e ˛ n EŒZ e
x PŒX
˛ n
:
(8.28)
So our goal is to bound EŒZ from above. For each i 2 I , define the random variable Zi WD e ˛.Xi p/ . Note that Z D Q i 2I Zi , that fZi gi 2I is a mutually independent family of random variables (see Theorem 8.12), and that for each i 2 I , we have EŒZi D e ˛.1
p/
p C e ˛.0
p/
q D pe ˛q C qe
˛p
:
It follows that hY
EŒZ D E
i Y Zi D EŒZi D .pe ˛q C qe
i2I
˛p n
/ :
i 2I
We will prove below that pe ˛q C qe
˛p
e˛
EŒZ e ˛
2 q n=2
2 q=2
:
(8.29)
From this, it follows that :
(8.30)
Combining (8.30) with (8.28), we obtain p e ˛
x PŒX
2 q n=2
˛ n
:
(8.31)
Now we choose the parameter ˛ so as to minimize the quantity ˛ 2 q n=2 ˛ n. The optimal value of ˛ is easily seen to be ˛ D =q, and substituting this value of ˛ into (8.31) yields (i). To finish the proof of the theorem, it remains to prove the inequality (8.29). Let ˇ WD pe ˛q C qe We want to show that ˇ e ˛
2 q=2
˛p
:
, or equivalently, that log ˇ ˛ 2 q=2. We have
ˇ D e ˛q .p C qe
˛
/ D e ˛q .1
q.1
e
˛
//;
and taking logarithms and applying parts (i) and (ii) of §A1, we obtain log ˇ D ˛q Clog.1 q.1 e
˛
// ˛q q.1 e
˛
/ D q.e
˛
C˛ 1/ q˛ 2 =2:
This establishes (8.29) and completes the proof of the theorem. Thus, the Chernoff bound is a quantitatively superior version of the law of large numbers, although its range of application is clearly more limited.
243
8.5 Some useful bounds
Example 8.33. Suppose we toss fair coin 10,000 times. The expected number of heads is 5,000. What is an upper bound on the probability ˛ that we get 6,000 or more heads? Using Markov’s inequality, we get ˛ 5=6. Using Chebyshev’s inequality, and in particular, the inequality (8.27), we get ˛
1=4 104 10 2
D
1 : 400
Finally, using the Chernoff bound, we obtain ˛e
104 10
2 =2.0:5/
De
100
10
43:4
:
E XERCISE 8.25. You are given three biased coins, where for i D 1; 2; 3, coin i comes up heads with probability pi . The coins look identical, and all you know is the following: (1) jp1 p2 j > 0:01 and (2) either p3 D p1 or p3 D p2 . You goal is to determine whether p3 is equal to p1 , or to p2 . Design a random experiment to determine this. The experiment may produce an incorrect result, but this should happen with probability at most 10 12 . Try to use a reasonable number of coin tosses. E XERCISE 8.26. Consider the following game, parameterized by a positive integer n. One rolls a pair of dice, and records the value of their sum. This is repeated until some value ` is recorded n times, and this value ` is declared the “winner.” It is intuitively clear that 7 is the most likely winner. Let ˛n be the probability that 7 does not win. Give a careful argument that ˛n ! 0 as n ! 1. Assume that the rolls of die are mutually independent. E XERCISE 8.27. With notation and assumptions as in Theorem 8.24, and with x 1=2j c=pn 1=2 for some positive constant p WD q WD 1=2, show that PŒjX c. Hint: use Exercise 5.16. E XERCISE 8.28. In a random walk, with every step, we toss a coin, and move either one unit to the right, or one unit to the left, depending on the outcome of the coin toss. The question is, after n steps, what is our expected distance from the starting point? Let us model this using random variables X1 ; : : : ; Xn , each uniformly distributed over f 1; 1g, and define X WD jX1 C C Xn j. Show that p (a) if fXi gniD1 is pairwise independent, then EŒX n; p (b) if fXi gniD1 is mutually independent, then EŒX c n for some positive constant c (hint: use the previous exercise). E XERCISE 8.29. The goal of this exercise is to prove that with probability very close to 1, a random number between 1 and m has very close to log log m prime
244
Finite and discrete probability distributions
factors. To prove this result, you will need to use appropriate theorems from Chapter 5. Suppose N is a random variable that is uniformly distributed over f1; : : : ; mg, where m 3. For i D 1; : : : ; m, let Di be the indicator variable for the event that i P divides N. Also, define X WD pm Dp , where the sum is over all primes p m, so that X counts the number of distinct primes dividing N. Show that: (a) 1=i (b) jEŒX
1=m < EŒDi 1= i , for each i D 1; : : : ; m; log log mj c1 for some constant c1 ;
(c) for all primes p; q, where p m, q m, and p ¤ q, we have 1 1 1 CovŒDp ; Dq C ; m p q where Cov is the covariance, as defined in Exercise 8.22; (d) VarŒX log log m C c2 for some constant c2 ; (e) for some constant c3 , and for every ˛ 1, we have h i P jX log log mj ˛.log log m/1=2 ˛ 2 1 C c3 .log log m/
1=2
:
8.6 Balls and bins This section and the next discuss applications of the theory developed so far. Our first application is a brief study of “balls and bins.” Suppose you throw n balls into m bins. A number of questions naturally arise, such as: What is the probability that a collision occurs, that is, two balls land in the same bin? What is the expected value of the maximum number of balls that land in any one bin? To formalize these questions, we introduce some notation that will be used throughout this section. Let I be a finite set of size n > 0, and S a finite set of size m > 0. Let fXi gi 2I be a family of random variables, where each Xi is uniformly distributed over the set S. The idea is that I represents a set of labels for our n balls, S represents the set of m bins, and Xi represents the bin into which ball i lands. We define C to be the event that a collision occurs; formally, this is the event that Xi D Xj for some i; j 2 I with i ¤ j . We also define M to be the random variable that measures that maximum number of balls in any one bin; formally, M WD maxfNs W s 2 Sg; where for each s 2 S, Ns is the number of balls that land in bin s; that is, Ns WD jfi 2 I W Xi D sgj:
245
8.6 Balls and bins
The questions posed above can now be stated as the problems of estimating PŒC and EŒM. However, to estimate these quantities, we have to make some assumptions about the independence of the Xi ’s. While it is natural to assume that the family of random variables fXi gi 2I is mutually independent, it is also interesting and useful to estimate these quantities under weaker independence assumptions. We shall therefore begin with an analysis under the weaker assumption that fXi gi 2I is pairwise independent. We start with a simple observation: Theorem 8.25. Suppose fXi gi 2I is pairwise independent. Then for all i; j 2 I with i ¤ j , we have PŒXi D Xj D 1=m. Proof. The event Xi D Xj occurs if and only if Xi D s and Xj D s for some s 2 S . Therefore, X PŒ.Xi D s/ \ .Xj D s/ (by Boole’s equality (8.7)) PŒXi D Xj D s2S
D
X
1=m2 (by pairwise independence)
s2S
D 1=m: Theorem 8.26. Suppose fXi gi 2I is pairwise independent. Then PŒC
n.n 1/ : 2m
Proof. Let I .2/ WD fJ I W jJ j D 2g. Then using Boole’s inequality (8.6) and Theorem 8.25, we have PŒC
X fi;j g2I .2/
PŒXi D Xj D
X fi;j g2I .2/
jI .2/ j n.n 1/ 1 D D : m m 2m
Theorem 8.27. Suppose fXi gi 2I is pairwise independent. Then q EŒM n2 =m C n: Proof. To prove this, we use the fact that EŒM2 EŒM 2 (see Theorem 8.19), and P that M 2 Z WD s2S Ns2 . It will therefore suffice to show that EŒZ n2 =m C n:
(8.32)
To this end, for i 2 I and s 2 S , let Li s be the indicator variable for the event that ball i lands in bin s (i.e., Xi D s), and for i; j 2 I , let Cij be the indicator variable for the event that balls i and j land in the same bin (i.e., Xi D Xj ). Observing that
246
Finite and discrete probability distributions
Cij D
P
s2S
ZD
X
Li s Ljs , we have X X 2 X X X X X Ns2 D Li s D Li s Ljs D Li s Ljs
s2S
D
X
s2S i 2I
s2S i 2I
j 2I
i;j 2I s2S
Cij :
i;j 2I
For i; j 2 I , we have EŒCij D PŒXi D Xj (see Theorem 8.16), and so by Theorem 8.25, we have EŒCij D 1=m if i ¤ j , and clearly, EŒCij D 1 if i D j . By linearity of expectation, we have X X X n.n 1/ C n n2 =m C n; EŒZ D EŒCij D EŒCij C EŒCi i D m i;j 2I
i;j 2I i ¤j
i 2I
which proves (8.32). We next consider the situation where fXi gi 2I is mutually independent. Of course, Theorem 8.26 is still valid in this case, but with our stronger assumption, we can derive a lower bound on PŒC. Theorem 8.28. Suppose fXi gi 2I is mutually independent. Then PŒC 1
e
n.n 1/=2m
:
x We want to show ˛ e n.n 1/=2m . We may assume Proof. Let ˛ WD PŒC. that I D f1; : : : ; ng (the labels make no difference) and that n m (otherwise, ˛ D 0). Under the hypothesis of the theorem, the random variable .X1 ; : : : ; Xn / is uniformly distributed over S n . Among all mn sequences .s1 ; : : : ; sn / 2 S n , there are a total of m.m 1/ .m n C 1/ that contain no repetitions: there are m choices for s1 , and for any fixed value of s1 , there are m 1 choices for s2 , and so on. Therefore 1 2 n 1 ˛ D m.m 1/ .m n C 1/=mn D 1 1 1 : m m m Using the part (i) of §A1, we obtain ˛e
Pn
1 iD1
i=m
De
n.n 1/=2m
:
Theorem 8.26 implies that if n.n 1/ m, then the probability of a collision is at most 1=2; moreover, Theorem 8.28 implies that if n.n 1/ .2 log 2/m, then p the probability of a collision is at least 1=2. Thus, for n near m, the probability of a collision is roughly 1=2. A colorful illustration of this is the following fact: in a room with 23 or more people, the odds are better than even that two people in the room have birthdays on the same day of the year. This follows by setting n D 23
8.6 Balls and bins
247
and m D 365 in Theorem 8.28. Here, we are ignoring leap years, and the fact that birthdays are not uniformly distributed over the calendar year (however, any skew in the birthday distribution only increases the odds that two people share the same birthday — see Exercise 8.34 below). Because of this fact, Theorem 8.28 is often called the birthday paradox (the “paradox” being the perhaps surprisingly small number of people in the room). The hypothesis that fXi gi 2I is mutually independent is essential in Theorem 8.28. Indeed, assuming just pairwise independence, we may have PŒC D 1=m, even when n D m (see Exercise 8.36 below). However, useful, non-trivial lower bounds on PŒC can still be obtained under assumptions weaker than mutual independence (see Exercise 8.37 below). Assuming fXi gi 2I is mutually independent, we can get a much sharper upper bound on EŒM than that provided by Theorem 8.27. For simplicity, we only consider the p case where m D n; in this case, Theorem 8.27 gives us the bound EŒM 2n (which cannot be substantially improved assuming only pairwise independence—see Exercise 8.38 below). Theorem 8.29. Suppose fXi gi 2I is mutually independent and that m D n. Then log n : log log n P Proof. We use Theorem 8.17, which says that EŒM D k1 PŒM k. Claim 1. For k 1, we have PŒM k n=kŠ. To prove Claim 1, we may assume that k n (as otherwise, PŒM k D 0). Let I .k/ WD fJ I W jJ j D kg. Now, M k if and only if there is an s 2 S and a subset J 2 I .k/ , such that Xj D s for all j 2 J . Therefore, i X X h\ P PŒM k .Xj D s/ (by Boole’s inequality (8.6)) EŒM .1 C o.1//
s2S J 2I .k/
D
j 2J
X X Y
PŒXj D s (by mutual independence)
s2S J 2I .k/ j 2J
! n n Dn k
k
n=kŠ:
That proves Claim 1. Of course, Claim 1 is only interesting when n=kŠ 1, since PŒM k is always at most 1. Define F .n/ to be the smallest positive integer k such that kŠ n. Claim 2. F .n/ log n= log log n. To prove this, let us set k WD F .n/. It is clear that n kŠ nk, and taking
248
Finite and discrete probability distributions
logarithms, log n log kŠ log n C log k. Moreover, we have Z k k X log kŠ D log ` D log x dx CO.log k/ D k log k k CO.log k/ k log k; 1
`D1
where we have estimated the sum by an integral (see §A5). Thus, log n D log kŠ C O.log k/ k log k. Taking logarithms again, we see that log log n D log k C log log k Co.1/ log k, and so log n k log k k log log n, from which Claim 2 follows. Finally, observe that each term in the sequence fn=kŠg1 is at most half the kD1 previous term. Combining this observation with Claims 1 and 2, and the fact that PŒM k is always at most 1, we have X X X EŒM D PŒM k D PŒM k C PŒM k k1
F .n/ C
kF .n/
X
2
`
k>F .n/
D F .n/ C 1 log n= log log n:
`1
E XERCISE 8.30. Let ˛1 ; : : : ; ˛m be real numbers that sum to 1. Show that 0 Pm P P 2 2 1=m/2 D m 1=m; and in particular, m sD1 .˛s sD1 ˛s sD1 ˛s 1=m: E XERCISE 8.31. Let X and X 0 be independent random variables, both having the P same distribution on a set S of size m. Show that PŒX D X 0 D s2S PŒX D s2 1=m: E XERCISE 8.32. Suppose that the family of random variables X ; Y ; Y 0 is mutually independent, where X has image S , and where Y and Y 0 have the same distribution on a set T . Let be a predicate on S T , and let ˛ WD PŒ .X ; Y /. Show that PŒ .X ; Y / \ .X ; Y 0 / ˛ 2 . In addition, show that if Y and Y 0 are both uniformly distributed over T , then PŒ .X ; Y /\ .X ; Y 0 /\.Y ¤ Y 0 / ˛ 2 ˛=jT j. E XERCISE 8.33. Let ˛1 ; : : : ; ˛m be non-negative real numbers that sum to 1. Let S WD f1; : : : ; mg, and for n D 1; : : : ; m, let S .n/ WD fT S W jT j D ng, and define X Y Pn .˛1 ; : : : ; ˛m / WD ˛t : T 2S .n/ t 2T
Show that Pn .˛1 ; : : : ; ˛m / is maximized when ˛1 D D ˛m D 1=m. Hint: first argue that if ˛s < ˛ t , then for every 2 Œ0; ˛ t ˛s , replacing the pair .˛s ; ˛ t / by .˛s C ; ˛ t / does not decrease the value of Pn .˛1 ; : : : ; ˛m /. E XERCISE 8.34. Suppose that fXi gi 2I is a finite, non-empty, mutually independent family of random variables, where each Xi is uniformly distributed over a
8.6 Balls and bins
249
finite set S . Suppose that fYi gi 2I is another finite, non-empty, mutually independent family of random variables, where each Yi has the same distribution and takes values in the set S . Let ˛ be the probability that the Xi ’s are distinct, and ˇ be the probability that the Yi ’s are distinct. Using the previous exercise, show that ˇ ˛. E XERCISE 8.35. Suppose n balls are thrown into m bins. Let A be the event that there is some bin that is empty. Assuming that the throws are mutually independent, and that n m.log m C t / for some t 0, show that PŒA e t . E XERCISE 8.36. Show that for every prime p, there exists a pairwise independent family of random variables fXi gi 2Zp , where each Xi is uniformly distributed over Zp , and yet the probability that all the Xi ’s are distinct is 1 1=p. E XERCISE 8.37. Let fXi gniD1 be a finite, non-empty, 4-wise independent family of random variables, each uniformly distributed over a set S . Let ˛ be the probability that the Xi ’s are distinct. For i; j D 1; : : : ; n, let Cij be the indicator variable for the event that Xi D Xj , and define K WD f.i; j / W 1 i n 1; i C 1 j ng P and Z WD .i;j /2K Cij : Show that: (a) fCij g.i;j /2K is pairwise independent; (b) EŒZ D n.n
1/=2m and VarŒZ D .1
1=m/EŒZ ;
(c) ˛ 1=EŒZ ; (d) ˛ 1=2, provided n.n
1/ 2m (hint: Exercise 8.4).
E XERCISE 8.38. Let k be a positive integer, let n WD k 2 k C1, let I and S be sets of size n, and let s0 be a fixed element of S . Also, let I .k/ WD fJ I W jJ j D kg, and let … be the set of all permutations on S . For each J 2 I .k/ , let J be some function that maps J onto s0 , and maps I n J injectively into S n fs0 g. For 2 …, J 2 I .k/ , and i 2 I , define i .; J / WD . J .i //. Finally, let Y be uniformly distributed over … I .k/ , and for i 2 I , define Xi WD i .Y /. Show that fXi gi 2I is pairwise independent, with each Xi uniformly distributed over S , and yet the p number of Xi ’s with the same value is always at least n. E XERCISE 8.39. Let S be a set of size m 1, and let s0 be an arbitrary, fixed element of S . Let F be a random variable that is uniformly distributed over the set of all mm functions from S into S . Let us define random variables Xi , for i D 0; 1; 2; : : : ; as follows: X0 WD s0 ; Xi C1 WD F .Xi / .i D 0; 1; 2; : : :/: Thus, the value of Xi is obtained by applying the function F a total of i times to the starting value s0 . Since S has size m, the sequence fXi g1 i D0 must repeat at some point; that is, there exists a positive integer n (with n m) such that Xn D Xi for
250
Finite and discrete probability distributions
some i D 0; : : : ; n 1. Define the random variable Y to be the smallest such value n. (a) Show that for every i 0 and for all s1 ; : : : ; si 2 S such that s0 ; s1 ; : : : ; si are distinct, the conditional distribution of Xi C1 given .X1 D s1 / \ \ .Xi D si / is the uniform distribution on S . (b) Show that for every integer n 1, we have Y n if and only if X0 ; X1 ; : : : ; Xn 1 take on distinct values. (c) From parts (a) and (b), show that for each n D 1; : : : ; m, we have PŒY n j Y n
1 D 1
.n
1/=m;
and conclude that PŒY n D
nY1
i=m/ e
.1
n.n 1/=2m
:
i D1
(d) Using part (c), show that X X e PŒY n EŒY D
n.n 1/=2m
D O.m1=2 /:
n1
n1
(e) Modify the above argument to show that EŒY D .m1=2 /. E XERCISE 8.40. The setup for this exercise is identical to that of the previous exercise, except that now, F is uniformly distributed over the set of all mŠ permutations of S . (a) Show that if Y D n, then Xn D X0 . (b) Show that for every i 0 and all s1 ; : : : ; si 2 S such that s0 ; s1 ; : : : ; si are distinct, the conditional distribution of Xi C1 given .X1 D s1 / \ \ .Xi D si / is essentially the uniform distribution on S n fs1 ; : : : ; si g. (c) Show that for each n D 2; : : : ; m, we have PŒY n j Y n
1 D 1
m and conclude that for all n D 1; : : : ; m, we have PŒY n D
nY2
1
i D0
1 m
i
D1
1 ; nC2 n
1 m
:
(d) From part (c), show that Y is uniformly distributed over f1; : : : ; mg, and in particular, EŒY D .m C 1/=2.
8.7 Hash functions
251
8.7 Hash functions In this section, we apply the tools we have developed thus far to a particularly important area of computer science: the theory and practice of hashing. Let R, S , and T be finite, non-empty sets. Suppose that for each r 2 R, we have a function r W S ! T . We call r a hash function (from S to T ). Elements of R are called keys, and if r .s/ D t, we say that s hashes to t under r. In applications of hash functions, we are typically interested in what happens when various inputs are hashed under a randomly chosen key. To model such situations, let H be a random variable that is uniformly distributed over R, and for each s 2 S, let us define the random variable H .s/, which takes the value r .s/ when H D r. We say that the family of hash functions f r gr2R is pairwise independent if the family of random variables f H .s/gs2S is pairwise independent, with each H .s/ uniformly distributed over T . We say thatf r gr2R is universal if PΠH .s/ D H .s 0 / 1=jT j
for all s; s 0 2 S with s ¤ s 0 . We make a couple of simple observations. First, by Theorem 8.25, if the family of hash functions f r gr2R is pairwise independent, then it is universal. Second, by Theorem 8.10, if jS j > 1, then f r gr2R is pairwise independent if and only if the following condition holds: the random variable . H .s/; H .s 0 // is uniformly distributed over T T , for all s; s 0 2 S with s ¤ s 0 . Before looking at constructions of pairwise independent and universal families of hash functions, we briefly discuss two important applications. Example 8.34. Suppose f r gr2R is a universal family of hash functions from S to T . One can implement a “dictionary” using a so-called hash table, which is basically an array A indexed by T , where each entry in A is a list. Entries in the dictionary are drawn from the set S . To insert a word s 2 S into the dictionary, s is first hashed to an index t , and then s is appended to the list AŒt ; likewise, to see if an arbitrary word s 2 S is in the dictionary, s is first hashed to an index t , and then the list AŒt is searched for s. Usually, the set of entries in the dictionary is much smaller than the set S. For example, S may consist of all bit strings of length up to, say 2048, but the dictionary may contain just a few thousand, or a few million, entries. Also, to be practical, the set T should not be too large. Of course, all entries in the dictionary could end up hashing to the same index,
252
Finite and discrete probability distributions
in which case, looking up a word in the dictionary degenerates into linear search. However, we hope that this does not happen, and that entries hash to indices that are nicely spread out over T . As we will now see, in order to ensure reasonable performance (in an expected sense), T needs to be of size roughly equal to the number of entries in the dictionary, Suppose we create a dictionary containing n entries. Let m WD jT j, and let I S be the set of entries (so n D jI j). These n entries are inserted into the hash table using a randomly chosen hash key, which we model as a random variable H that is uniformly distributed over R. For each s 2 S , we define the random variable Ls to be the number of entries in I that hash to the same index as s under the key H; that is, Ls WD jfi 2 I W H .s/ D H .i /gj. Intuitively, Ls measures the cost of looking up the particular word s in the dictionary. We want to bound EŒLs . To this P end, we write Ls as a sum of indicator variables: Ls D i 2I Csi , where Csi is the indicator variable for the event that H .s/ D H .i /. By Theorem 8.16, we have EŒCsi D PŒ H .s/ D H .i /; moreover, by the universal property, EŒCsi 1=m if s ¤ i , and clearly, EŒCsi D 1 if s D i . By linearity of expectation, we have X EŒCsi : EŒLs D i 2I
If s … I , then each term in the sum is 1=m, and so EŒLs n=m. If s 2 I , then one term in the sum is 1, and the other n 1 terms are 1=m, and so EŒLs 1 C .n 1/=m. In any case, we have EŒLs 1 C n=m:
In particular, this means that if m n, then the expected cost of looking up any particular word in the dictionary is bounded by a constant. Example 8.35. Suppose Alice wants to send a message to Bob in such a way that Bob can be reasonably sure that the message he receives really came from Alice, and was not modified in transit by some malicious adversary. We present a solution to this problem here that works assuming that Alice and Bob share a randomly generated secret key, and that this key is used to authenticate just a single message (multiple messages can be authenticated using multiple keys). Suppose that f r gr2R is a pairwise independent family of hash functions from S to T . We model the shared random key as a random variable H, uniformly distributed over R. We also model Alice’s message as a random variable X , taking values in the set S. We make no assumption about the distribution of X , but we do assume that X and H are independent. When Alice sends the message X to Bob, she also sends the “authentication tag” Y WD H .X /. Now, when Bob receives a message X 0 and tag Y 0 , he checks that H .X 0 / D Y 0 ; if this holds, he accepts the
8.7 Hash functions
253
message X 0 as authentic; otherwise, he rejects it. Here, X 0 and Y 0 are also random variables; however, they may have been created by a malicious adversary who may have even created them after seeing X and Y . We can model such an adversary as a pair of functions f W S T ! S and g W S T ! T , so that X 0 WD f .X ; Y / and Y 0 WD g.X ; Y /. The idea is that after seeing X and Y , the adversary computes X 0 and Y 0 and sends X 0 and Y 0 to Bob instead of X and Y . Let us say that the adversary fools Bob if H .X 0 / D Y 0 and X 0 ¤ X . We will show that PŒF 1=m, where F is the event that the adversary fools Bob, and m WD jT j. Intuitively, this bound holds because the pairwise independence property guarantees that after seeing the value of H at one input, the value of H at any other input is completely unpredictable, and cannot be guessed with probability any better than 1=m. If m is chosen to be suitably large, the probability that Bob gets fooled can be made acceptably small. For example, S may consist of all bit strings of length up to, say, 2048, while the set T may be encoded using much shorter bit strings, of length, say, 64. This is nice, as it means that the authentication tags consume very little additional bandwidth. The claim that PŒF 1=m may be rigorously justified by a straightforward calculation: i XX h P .X D s/ \ .Y D t / \ F (total probability (8.9)) PŒF D s2S t 2T
D
XX h
P .X D s/ \ . H .s/ D t / \ . H .f .s; t // D g.s; t //
s2S t 2T
i \ .f .s; t / ¤ s/ h XX D PŒX D s P . H .s/ D t / \ . H .f .s; t // D g.s; t // i s2S t 2T \ .f .s; t / ¤ s/ (since X and H are independent) XX PŒX D s .1=m2 / (since f r gr2R is pairwise independent) s2S t 2T
D .1=m/
X
PŒX D s D 1=m:
s2S
We now present several constructions of pairwise independent and universal families of hash functions. Example 8.36. By setting k WD 2 in Example 8.27, for every prime p, we immediately get a pairwise independent family of hash functions f r gr2R from Zp to Zp , where R D Zp Zp , and for r D .r0 ; r1 / 2 R, the hash function r is given by r W Zp ! Zp s 7! r0 C r1 s:
254
Finite and discrete probability distributions
While very simple and elegant, the family of hash functions in Example 8.36 is not very useful in practice. As we saw in Examples 8.34 and 8.35, what we would really like are families of hash functions that hash long inputs to short outputs. The next example provides us with a pairwise independent family of hash functions that satisfies this requirement. Example 8.37. Let p be a prime, and let ` be a positive integer. Let S WD Zp` and .`C1/
R WD Zp
. For each r D .r0 ; r1 ; : : : ; r` / 2 R, we define the hash function r W
S ! Zp .s1 ; : : : ; s` / 7! r0 C r1 s1 C C r` s` :
We will show that f r gr2R is a pairwise independent family of hash functions from S to Zp . To this end, let H be a random variable uniformly distributed over R. We want to show that for each s; s 0 2 S with s ¤ s 0 , the random variable . H .s/; H .s 0 // is uniformly distributed over Zp Zp . So let s ¤ s 0 be fixed, and define the function W R ! Zp Zp r 7! . r .s/; r .s 0 //: Because is a group homomorphism, it will suffice to show that is surjective (see Theorem 8.5). Suppose s D .s1 ; : : : ; s` / and s 0 D .s10 ; : : : ; s`0 /. Since s ¤ s 0 , we must have sj ¤ sj0 for some j D 1; : : : ; `. For this j , consider the function 0 W
R ! Zp Zp .r0 ; r1 ; : : : ; r` / 7! .r0 C rj sj ; r0 C rj sj0 /:
Evidently, the image of includes the image of 0 , and by Example 8.36, the function 0 is surjective. To use the construction in Example 8.37 in applications where the set of inputs consists of bits strings of a given length, one can naturally split such a bit string up into short bit strings which, when viewed as integers, lie in the set f0; : : : ; p 1g, and which can in turn be viewed as elements of Zp . This gives us a natural, injective map from bit strings to elements of Zp` . The appropriate choice of the prime p depends on the application. Of course, the requirement that p is prime limits our choice in the size of the output set; however, this is usually not a severe restriction, as Bertrand’s postulate (Theorem 5.8) tells us that we can always choose p to within a factor of 2 of any desired value of the output set size. Nevertheless, the construction in following example gives us a universal (but not pairwise independent) family of hash functions with an output set of any size we wish. Example 8.38. Let p be a prime, and let m be an arbitrary positive integer. Let
255
8.7 Hash functions
us introduce some convenient shorthand notation: for ˛ 2 Zp , let ŒŒ˛m WD Œrep.˛/m 2 Zm (recall that rep.˛/ denotes the unique integer a 2 f0; : : : ; p 1g such that ˛ D Œap ). Let R WD Zp Zp , and for each r D .r0 ; r1 / 2 R, define the hash function r W Zp ! Zm s 7! ŒŒr0 C r1 sm : Our goal is to show that f r gr2R is a universal family of hash functions from Zp to Zm . So let s; s 0 2 Zp with s ¤ s 0 , let H0 and H1 be independent random variables, with H0 uniformly distributed over Zp and H1 uniformly distributed over Zp , and let H WD .H0 ; H1 /. Also, let C be the event that H .s/ D H .s 0 /. We want to show that PŒC 1=m. Let us define random variables Y WD H0 C H1 s and Y 0 WD H0 C H1 s 0 . Also, let sO WD s 0 s ¤ 0. Then we have h i PŒC D P ŒŒY m D ŒŒY 0 m h i D P ŒŒY m D ŒŒY C H1 sO m (since Y 0 D Y C H1 sO ) i X h D P ŒŒY m D ŒŒY C H1 sO m \ .Y D ˛/ (total probability (8.9)) ˛2Zp
D
X
h
P ŒŒ˛m D ŒŒ˛ C H1 sO m \ .Y D ˛/
i
˛2Zp
D
X
h
i
P ŒŒ˛m D ŒŒ˛ C H1 sO m PŒY D ˛
˛2Zp
(by Theorem 8.13, Y and H1 are independent). It will suffice to show that h
i
P ŒŒ˛m D ŒŒ˛ C H1 sO m 1=m
(8.33)
for each ˛ 2 Zp , since then X X PŒC .1=m/PŒY D ˛ D .1=m/ PŒY D ˛ D 1=m: ˛2Zp
˛2Zp
So consider a fixed ˛ 2 Zp . As sO ¤ 0 and H1 is uniformly distributed over Zp , it follows that H1 sO is uniformly distributed over Zp , and hence ˛ C H1 sO is uniformly distributed over the set Zp n f˛g. Let M˛ WD fˇ 2 Zp W ŒŒ˛m D ŒŒˇm g. To prove (8.33), we need to show that jM˛ n f˛gj .p 1/=m. But it is easy to see that jM˛ j dp=me, and since M˛ certainly contains ˛, we have lpm p m 1 p 1 1 C 1D : jM˛ n f˛gj m m m m
256
Finite and discrete probability distributions
One drawback of the family of hash functions in the previous example is that the prime p may need to be quite large (at least as large as the size of the set of inputs) and so to evaluate a hash function, we have to perform modular multiplication of large integers. In contrast, in Example 8.37, the prime p can be much smaller (only as large as the size of the set of outputs), and so these hash functions can be evaluated much more quickly. Another consideration in designing families of hash functions is the size of key set. The following example gives a variation of the family in Example 8.37 that uses somewhat a smaller key set (relative to the size of the input), but is only a universal family, and not a pairwise independent family. .`C1/ Example 8.39. Let p be a prime, and let ` be a positive integer. Let S WD Zp and R WD Zp` . For each r D .r1 ; : : : ; r` / 2 R, we define the hash function
r W
S ! Zp .s0 ; s1 ; : : : ; s` / 7! s0 C r1 s1 C C r` s` :
Our goal is to show that f r gr2R is a universal family of hash functions from S to Zp . So let s; s 0 2 S with s ¤ s 0 , and let H be a random variable that is uniformly distributed over R. We want to show that PŒ H .s/ D H .s 0 / 1=p. Let s D .s0 ; s1 ; : : : ; s` / and s 0 D .s00 ; s10 ; : : : ; s`0 /, and set sOi WD si0 si for i D 0; 1; : : : ; `. Let us define the function W
R ! Zp .r1 ; : : : ; r` / 7! r1 sO1 C C r` sO` :
Clearly, H .s/ D H .s 0 / if and only if .H/ D sO0 . Moreover, is a group homomorphism. There are two cases two consider. In the first case, sOi D 0 for all i D 1; : : : ; `; in this case, the image of is f0g, but sO0 ¤ 0 (since s ¤ s 0 ), and so PŒ.H/ D sO0 D 0. In the second case, sOi ¤ 0 for some i D 1; : : : ; `; in this case, the image of is Zp , and so .H/ is uniformly distributed over Zp (see Theorem 8.5); thus, PŒ.H/ D sO0 D 1=p. One can get significantly smaller key sets, if one is willing to relax the definitions of universal and pairwise independence. Let f r gr2R be a family of hash functions from S to T , where m WD jT j. Let H be a random variable that is uniformly distributed over R. We say that f r gr2R is -almost universal if for all s; s 0 2 S with s ¤ s 0 , we have PŒ H .s/ D H .s 0 / . Thus, f r gr2R is universal if and only if it is 1=m-almost universal. We say that f r gr2R is -almost strongly universal if H .s/ is uniformly distributed over T for each s 2 S , and PŒ. H .s/ D t / \ . H .s 0 / D t 0 / =m for all s; s 0 2 S with s ¤ s 0 and all t; t 0 2 T . Constructions, properties, and applications of these types of hash functions are developed in some of the exercises below.
8.7 Hash functions
257
E XERCISE 8.41. For each positive integer n, let In denote f0; : : : ; n 1g. Let m ` , and R WD I .`C1/ . For be a power of a prime, ` be a positive integer, S WD Im m2 each r D .r0 ; r1 ; : : : ; r` / 2 R, define the hash function r W
S ! Im j k .s1 ; : : : ; s` / 7! r0 C r1 s1 C C r` s` mod m2 =m :
Using the result from Exercise 2.13, show that f r gr2R is a pairwise independent family of hash functions from S to Im . Note that on a typical computer, if m is a suitable power of 2, then it is very easy to evaluate these hash functions, using just multiplications, additions, shifts, and masks (no divisions). E XERCISE 8.42. Let f r gr2R be an -almost universal family of hash functions from S to T . Also, let H; X ; X 0 be random variables, where H is uniformly distributed over R, and both X and X 0 take values in S . Moreover, assume H and .X ; X 0 / are independent. Show that PŒ H .X / D H .X 0 / PŒX D X 0 C . E XERCISE 8.43. Let f r gr2R be an -almost universal a family of hash functions from S to T , and let H be a random variable that is uniformly distributed over R. Let I be a subset of S of size n > 0. Let C be the event that H .i / D H .j / for some i; j 2 I with i ¤ j . We define several random variables: for each t 2 T , N t WD jfi 2 I W H .i / D t gj; M WD maxfN t W t 2 T g; for each s 2 S, Ls WD jfi 2 I W H .s/ D H .i /gj. Show that: (a) PŒC n.n 1/=2; p (b) EŒM n2 C n; (c) for each s 2 S , EŒLs 1 C n. The results of the previous exercise show that for many applications, the almost universal property is good enough, provided is suitably small. The next three exercises develop -almost universal families of hash functions with very small sets of keys, even when is quite small. E XERCISE 8.44. Let p be a prime, and let ` be a positive integer. Let S WD .`C1/ Zp . For each r 2 Zp , define the hash function r W
S ! Zp .s0 ; s1 ; : : : ; s` / 7! s0 C s1 r C C s` r ` :
Show that f r gr2Zp is an `=p-almost universal family of hash functions from S to Zp . E XERCISE 8.45. Let f r gr2R be an -almost universal family of hash functions
258
Finite and discrete probability distributions
from S to T . Let f r0 0 gr 0 2R0 is an 0 -almost universal family of hash functions from S 0 to T 0 , where T S 0 . Show that f r0 0 B r g.r;r 0 /2RR0 is an . C 0 /-almost universal family of hash functions from S to T 0 (here, “B” denotes function composition). E XERCISE 8.46. Let m and ` be positive integers, and let 0 < ˛ < 1. Given these parameters, show how to construct an -almost universal family of hash functions f r gr2R from Z` m to Zm , such that .1 C ˛/=m and log jRj D O.log m C log ` C log.1=˛//. Hint: use the previous two exercises, and Example 8.38. E XERCISE 8.47. Let f r gr2R be an -almost universal from S to T . Show that 1=jT j 1=jSj: E XERCISE 8.48. Let f r gr2R be a family of hash functions from S to T , with m WD jT j. Show that: (a) if f r gr2R is -almost strongly universal, then it is -almost universal; (b) if f r gr2R is pairwise independent, then it is 1=m-almost strongly universal; (c) if f r gr2R is -almost universal, and f r0 0 gr 0 2R0 is an 0 -almost strongly universal family of hash functions from S 0 to T 0 , where T S 0 , then f r0 0 B r g.r;r 0 /2RR0 is an . C 0 /-almost strongly universal family of hash functions from S to T 0 . E XERCISE 8.49. Show that if an -almost strongly universal family of hash functions is used in Example 8.35, then Bob gets fooled with probability at most . E XERCISE 8.50. Show how to construct an -almost strongly universal family of hash functions satisfying the same bounds as in Exercise 8.46, under the restriction that m is a prime power. E XERCISE 8.51. Let p be a prime, and let ` be a positive integer. Let S WD Zp` and R WD Zp Zp . For each .r0 ; r1 / 2 R, define the hash function r W
S ! Zp .s1 ; : : : ; s` / 7! r0 C s1 r1 C C s` r1` :
Show that f r gr2R is an `=p-almost strongly universal family of hash functions from S to Zp .
259
8.8 Statistical distance
8.8 Statistical distance This section discusses a useful measure of “distance” between two random variables. Although important in many applications, the results of this section (and the next) will play only a very minor role in the remainder of the text. Let X and Y be random variables which both take values in a finite set S . We define the statistical distance between X and Y as ˇ 1 X ˇˇ ŒX I Y WD PŒX D s PŒY D sˇ: 2 s2S
Theorem 8.30. For random variables X ; Y ; Z , we have (i) 0 ŒX I Y 1, (ii) ŒX I X D 0, (iii) ŒX I Y D ŒY I X , and (iv) ŒX I Z ŒX I Y C ŒY I Z . Proof. Exercise. It is also clear from the definition that ŒX I Y depends only on the distributions of X and Y , and not on any other properties. As such, we may sometimes speak of the statistical distance between two distributions, rather than between two random variables. Example 8.40. Suppose X has the uniform distribution on f1; : : : ; mg, and Y has the uniform distribution on f1; : : : ; m ıg, where ı 2 f0; : : : ; m 1g. Let us compute ŒX I Y . We could apply the definition directly; however, consider the following graph of the distributions of X and Y : 1=.m ! ı/
A
1=m B 0
C m!ı
m
The statistical distance between X and Y is just 1=2 times the area of regions A and C in the diagram. Moreover, because probability distributions sum to 1, we must have area of B C area of A D 1 D area of B C area of C ; and hence, the areas of region A and region C are the same. Therefore, ŒX I Y D area of A D area of C D ı=m:
260
Finite and discrete probability distributions
The following characterization of statistical distance is quite useful: Theorem 8.31. Let X and Y be random variables taking values in a set S . For every S 0 S , we have ŒX I Y jPŒX 2 S 0
PŒY 2 S 0 j;
and equality holds for some S 0 S (in particular, the set S 0 WD fs 2 S W PŒX D s < PŒY D sg, as well as its complement). Proof. Suppose we split the set S into two disjoint subsets: the set S0 consisting of those s 2 S such that PŒX D s < PŒY D s, and the set S1 consisting of those s 2 S such that PŒX D s PŒY D s. Consider the following rough graph of the distributions of X and Y , where the elements of S0 are placed to the left of the elements of S1 :
Y
A
X
C
B
S0
S1
Now, as in Example 8.40, ŒX I Y D area of A D area of C : Now consider any subset S 0 of S, and observe that PŒX 2 S 0 PŒY 2 S 0 D area of C 0 area of A0 , where C 0 is the subregion of C that lies above S 0 , and A0 is the subregion of A that lies above S 0 . It follows that jPŒX 2 S 0 PŒY 2 S 0 j is maximized when S 0 D S0 or S 0 D S1 , in which case it is equal to ŒX I Y . We can restate Theorem 8.31 as follows: ŒX I Y D maxfjPŒ .X /
PΠ.Y /j W is a predicate on S g:
This implies that when ŒX I Y is very small, then for every predicate , the events .X / and .Y / occur with almost the same probability. Put another way, there is no “statistical test” that can effectively distinguish between the distributions of X and Y . For many applications, this means that the distribution of X is “for all practical purposes” equivalent to that of Y , and hence in analyzing the behavior of X , we can instead analyze the behavior of Y , if that is more convenient.
261
8.8 Statistical distance
Theorem 8.32. If S and T are finite sets, X and Y are random variables taking values in S , and f W S ! T is a function, then Œf .X /I f .Y / ŒX I Y . Proof. By Theorem 8.31, for every subset T 0 of T , we have jPŒf .X / 2 T 0
PŒf .Y / 2 T 0 j D
jPŒX 2 f
1
.T 0 /
PŒY 2 f
1
.T 0 /j ŒX I Y :
In particular, again by Theorem 8.31, Œf .X /I f .Y / D jPŒf .X / 2 T 0
PŒf .Y / 2 T 0 j
for some T 0 . Example 8.41. Let X be uniformly distributed over the set f0; : : : ; m 1g, and let Y be uniformly distributed over the set f0; : : : ; n 1g, for n m. Let f .t / WD t mod m. We want to compute an upper bound on the statistical distance between X and f .Y /. We can do this as follows. Let n D q m r, where 0 r < m, so that q D dn=me. Also, let Z be uniformly distributed over f0; : : : ; q m 1g. Then f .Z / is uniformly distributed over f0; : : : ; m 1g, since every element of f0; : : : ; m 1g has the same number (namely, q) of pre-images under f which lie in the set f0; : : : ; q m 1g. Since statistical distance depends only on the distributions of the random variables, by the previous theorem, we have ŒX I f .Y / D Œf .Z /I f .Y / ŒZ I Y ; and as we saw in Example 8.40, ŒZ I Y D r =q m < 1=q m=n: Therefore, ŒX I f .Y / < m=n: We close this section with two useful theorems. Theorem 8.33. Suppose X , Y , and Z are random variables, where X and Z are independent, and Y and Z are independent. Then ŒX ; Z I Y ; Z D ŒX ; Y : Note that ŒX ; Z I Y ; Z is shorthand for Œ.X ; Z /I .Y ; Z /. Proof. Suppose X and Y take values in a finite set S, and Z takes values in a finite
262
Finite and discrete probability distributions
set T . From the definition of statistical distance, Xˇ ˇPŒ.X D s/ \ .Z D t / 2ŒX ; Z I Y ; Z D
ˇ
PŒ.Y D s/ \ .Z D t /ˇ
s;t
Xˇ ˇPŒX D sPŒZ D t D
ˇ
PŒY D sPŒZ D t ˇ
s;t
(by independence) X ˇ D PŒZ D t ˇPŒX D s
ˇ
PŒY D sˇ
s;t
D
X
X ˇ ˇPŒX D s
PŒZ D t
ˇ
PŒY D sˇ
s
t
D 1 2ŒX I Y : Theorem 8.34. Let X1 ; : : : ; Xn ; Y1 ; : : : ; Yn be random variables, where fXi gniD1 is mutually independent, and fYi gniD1 is mutually independent. Then we have ŒX1 ; : : : ; Xn I Y1 ; : : : ; Yn
n X
ŒXi I Yi :
i D1
Proof. Since ŒX1 ; : : : ; Xn I Y1 ; : : : ; Yn depends only on the distributions of .X1 ; : : : ; Xn / and .Y1 ; : : : ; Yn /, without loss of generality, we may assume that .X1 ; : : : ; Xn / and .Y1 ; : : : ; Yn / are independent, so that X1 ; : : : ; Xn ; Y1 ; : : : ; Yn form a mutually independent family of random variables. We introduce random variables Z0 ; : : : ; Zn , defined as follows: Z0 WD .X1 ; : : : ; Xn /; Zi WD .Y1 ; : : : ; Yi ; XiC1 ; : : : ; Xn / for i D 1; : : : ; n
1, and
Zn WD .Y1 ; : : : ; Yn /: By definition, ŒX1 ; : : : ; Xn I Y1 ; : : : ; Yn D ŒZ0 I Zn . Moreover, by part (iv) of P Theorem 8.30, we have ŒZ0 I Zn niD1 ŒZi 1 I Zi : Now consider any fixed index i D 1; : : : ; n. By Theorem 8.33, we have ŒZi
1 I Zi
D ΠXi ; .Y1 ; : : : ; Yi
1 ; Xi C1 ; : : : ; Xn /I
Yi ; .Y1 ; : : : ; Yi
1 ; Xi C1 ; : : : ; Xn /
D ŒXi I Yi : The theorem now follows immediately. The technique used in the proof of the previous theorem is sometimes called a hybrid argument, as one considers the sequence of “hybrid” variables
263
8.8 Statistical distance
Z0 ; Z1 ; : : : ; Zn , and shows that the distance between each consecutive pair of variables is small. E XERCISE 8.52. Let X and Y be independent random variables, each uniformly distributed over Zp , where p is prime. Calculate ŒX ; Y I X ; XY . E XERCISE 8.53. Let n be an integer that is the product of two distinct primes of the same bit length. Let X be uniformly distributed over Zn , and let Y be uniformly distributed over Zn . Show that ŒX I Y 3n 1=2 . E XERCISE 8.54. Let X and Y be 0/1-valued random variables. Show that ŒX I Y D jPŒX D 1 PŒY D 1j. E XERCISE 8.55. Let S be a finite set, and consider any function W S ! f0; 1g. Let B be a random variable uniformly distributed over f0; 1g, and for b D 0; 1, let Xb be a random variable taking values in S, and assume that Xb and B are independent. Show that jPŒ .XB / D B
1 2j
D 12 jPΠ.X0 / D 1
PΠ.X1 / D 1j
1 2 ŒX0 I X1 :
E XERCISE 8.56. Let X ; Y be random variables taking values in a finite set S . For an event B that occurs with non-zero probability, define the conditional statistical distance ˇ 1 X ˇˇ PŒX D s j B PŒY D s j Bˇ: ŒX I Y j B WD 2 s2S
Let fBi gi 2I be a finite, pairwise disjoint family of events whose union is B. Show that X ŒX I Y j BPŒB ŒX I Y j Bi PŒBi : PŒBi ¤0
E XERCISE 8.57. Let f r gr2R be a family hash functions from S to T , with m WD jT j. We say f r gr2R is -variationally universal if H .s/ is uniformly distributed over T for each s 2 S , and Œ H .s 0 /I Y j H .s/ D t for each s; s 0 2 S with s ¤ s 0 and each t 2 T ; here, H and Y are independent random variables, with H uniformly distributed over R, and Y uniformly distributed over T . Show that: (a) if f r gr2R is pairwise independent, then it is 0-variationally universal; (b) if f r gr2R is -variationally universal, then it is .1=m C /-almost strongly universal; (c) if f r gr2R is -almost universal, and f r0 0 gr 0 2R0 is an 0 -variationally universal family of hash functions from S 0 to T 0 , where T S 0 , then
264
Finite and discrete probability distributions
f r0 0 B r g.r;r 0 /2RR0 is an . C 0 /-variationally universal family of hash functions from S to T 0 . E XERCISE 8.58. Let f r gr2R be a family hash functions from S to T such that (i) each r maps S injectively into T , and (ii) there exists 2 Œ0; 1 such that Œ H .s/I H .s 0 / for all s; s 0 2 S , where H is uniformly distributed over R. Show that jRj .1 /jS j. E XERCISE 8.59. Let X and Y be random variables that take the same value unless x Show that ŒX I Y a certain event F occurs (i.e., X .!/ D Y .!/ for all ! 2 F). PŒF. E XERCISE 8.60. Let X and Y be random variables taking values in the interval Œ0; t . Show that jEŒX EŒY j t ŒX I Y . E XERCISE 8.61. Show that Theorem 8.33 is not true if we drop the independence assumptions. E XERCISE 8.62. Let S be a set of size m 1. Let F be a random variable that is uniformly distributed over the set of all functions from S into S . Let G be a random variable that is uniformly distributed over the set of all permutations of S . Let s1 ; : : : ; sn be distinct, fixed elements of S . Show that n.n 1/ : 2m E XERCISE 8.63. Let m be a large integer. Consider three random experiments. In the first, we generate a random integer X1 between 1 and m, and then a random integer Y1 between 1 and X1 . In the second, we generate a random integer X2 between 2 and m, and then generate a random integer Y2 between 1 and X2 . In the third, we generate a random integer X3 between 2 and m, and then a random integer Y3 between 2 and X3 . Show that ŒX1 ; Y1 I X2 ; Y2 D O.1=m/, and ŒX2 ; Y2 I X3 ; Y3 D O.log m=m/, and conclude that ŒX1 ; Y1 I X3 ; Y3 D O.log m=m/. ŒF .s1 /; : : : ; F .sn /I G .s1 /; : : : ; G .sn /
8.9 Measures of randomness and the leftover hash lemma ./ In this section, we discuss different ways to measure “how random” the distribution of a random variable is, and relations among them. Let X be a random variable taking values in a finite set S of size m. We define three measures of randomness: P 1. the collision probability of X is s2S PŒX D s2 ; 2. the guessing probability of X is maxfPŒX D s W s 2 S g;
8.9 Measures of randomness and the leftover hash lemma ./
3. the distance of X from uniform on S is
1P
2
s2S
jPŒX D s
265
1=mj:
Suppose X has collision probability ˇ, guessing probability , and distance ı from uniform on S. If X 0 is another random variable with the same distribution as X , where X and X 0 independent, then ˇ D PŒX D X 0 (see Exercise 8.31). If Y is a random variable that is uniformly distributed over S , then ı D ŒX I Y . If X itself is uniformly distributed over S , then ˇ D D 1=m, and ı D 0. The quantity log2 .1= / is sometimes called the min entropy of X , and the quantity log2 .1=ˇ/ is sometimes called the Renyi entropy of X . We first state some easy inequalities: Theorem 8.35. Suppose X is a random variable that takes values in a finite set S of size m. If X has collision probability ˇ, guessing probability , and distance ı from uniform on S , then: (i) ˇ 1=m; (ii) 2 ˇ 1=m C ı: Proof. Part (i) is immediate from Exercise 8.31. The other inequalities are left as easy exercises. This theorem implies that the collision and guessing probabilities are minimal for the uniform distribution, which perhaps agrees with ones intuition. While the above theorem implies that ˇ and are close to 1=m when ı is small, the following theorem provides a converse: Theorem 8.36. Suppose X is a random variable that takes values in a finite set S of sizep m. If X has collision probability ˇ, and distance ı from uniform on S , then ı 12 mˇ 1. Proof. We may assume that ı > 0, since otherwise the theorem is already true, simply from the fact that ˇ 1=m. P For s 2 S , let ps WD PŒX D s. We have ı D 21 s jps 1=mj, and hence P 1 D s qs , where qs WD jps 1=mj=2ı. So we have X 1 qs2 (by Exercise 8.30) m s 1 X D 2 .ps 1=m/2 4ı s 1 X 2 D 2. ps 1=m/ (again by Exercise 8.30) 4ı s D
1 .ˇ 4ı 2
1=m/;
266
Finite and discrete probability distributions
from which the theorem follows immediately. We are now in a position to state and prove a very useful result which, intuitively, allows us to convert a “low quality” source of randomness into a “high quality” source of randomness, making use of an almost universal family of hash functions (see end of §8.7). Theorem 8.37 (Leftover hash lemma). Let f r gr2R be a .1 C ˛/=m-almost universal family of hash functions from S to T , where m WD jT j. Let H and X be independent random variables, where H is uniformly distributed over R, and X takes values in S . If ˇ is the collision probability of X , and ı 0 is the distance of p .H; H .X // from uniform on R T , then ı 0 21 mˇ C ˛. Proof. Let ˇ 0 be the collision probability of .H; H .X //. Our goal is to bound ˇ 0 from above, and then apply Theorem 8.36 to the random variable .H; H .X //. To this end, let ` WD jRj, and suppose H 0 and X 0 are random variables, where H 0 has the same distribution as H, X 0 has the same distribution as X , and H; H 0 ; X ; X 0 form a mutually independent family of random variables. Then we have ˇ 0 D PŒ.H D H 0 / \ . H .X / D H 0 .X 0 // D PŒ.H D H 0 / \ . H .X / D H .X 0 // 1 D PŒ H .X / D H .X 0 / (a special case of Exercise 8.14) ` 1 .PŒX D X 0 C .1 C ˛/=m/ (by Exercise 8.42) ` 1 D .mˇ C 1 C ˛/: `m The theorem now follows immediately from Theorem 8.36. Note that in the above theorem, if f r gr2R is a universal family of hash functions, then we can take ˛ D 0. However, it is convenient to allow ˛ > 0, as this allows for the use of families with a smaller key set (see Exercise 8.46). Example 8.42. Suppose S WD f0; 1g1000 , T WD f0; 1g64 , and that f r gr2R is a universal family of hash functions from S to T . Suppose X and H are independent random variables, where X is uniformly distributed over some subset S 0 of S of size 2160 , and H is uniformly distributed over R. Then the collision and guessing probabilities of X are at most 2 160 , and so the leftover hash lemma (with ˛ D 0) says that the distance of .H; H .X // from uniform on R T is ı 0 , where ı 0 p 1 64 160 D 2 49 . By Theorem 8.32, it follows that the distance of .X / H 2 2 2 from uniform on T is at most ı 0 2 49 . The leftover hash lemma allows one to convert “low quality” sources of ran-
8.9 Measures of randomness and the leftover hash lemma ./
267
domness into “high quality” sources of randomness. Suppose that to conduct an experiment, we need to sample a random variable Y whose distribution is uniform on a set T of size m, or at least, its distance from uniform on T is sufficiently small. However, we may not have direct access to a source of “real” randomness whose distribution looks anything like that of the desired uniform distribution, but rather, only to a “low quality” source of randomness. For example, one could model various characteristics of a person’s typing at the keyboard, or perhaps various characteristics of the internal state of a computer (both its software and hardware) as a random process. We cannot say very much about the probability distributions associated with such processes, but perhaps we can conservatively estimate the collision or guessing probability associated with these distributions. Using the leftover hash lemma, we can hash the output of this random process, using a suitably generated random hash function. The hash function acts like a “magnifying glass”: it “focuses” the randomness inherent in the “low quality” source distribution onto the set T , obtaining a “high quality,” nearly uniform, distribution on T . Of course, this approach requires a random hash function, which may be just as difficult to generate as a random element of T . The following theorem shows, however, that we can at least use the same “magnifying glass” many times over, with the statistical distance from uniform of the output distribution increasing linearly in the number of applications of the hash function. Theorem 8.38. Let f r gr2R be a .1 C ˛/=m-almost universal family of hash functions from S to T , where m WD jT j. Let H; X1 ; : : : ; Xn be random variables, where H is uniformly distributed over R, each Xi takes values in S , and H; X1 ; : : : ; Xn form a mutually independent family of random variables. If ˇ is an upper bound on the collision probability of each Xi , and ı 0 is p the distance of .H; H .X1 /; : : : ; H .Xn // from uniform on R T n , then ı 0 12 n mˇ C ˛. Proof. Let Y1 ; : : : ; Yn be random variables, each uniformly distributed over T , and assume that H; X1 ; : : : ; Xn ; Y1 ; : : : ; Yn form a mutually independent family of random variables. We shall make a hybrid argument (as in the proof of Theorem 8.34). Define random variables Z0 ; Z1 ; : : : ; Zn as follows: Z0 WD .H; H .X1 /; : : : ; H .Xn //; Zi WD .H; Y1 ; : : : ; Yi ; H .Xi C1 /; : : : ; H .Xn // for i D 1; : : : ; n Zn WD .H; Y1 ; : : : ; Yn /:
1, and
268
Finite and discrete probability distributions
We have ı 0 D ŒZ0 I Zn
n X i D1 n X i D1
ŒZi
1 I Zi
(by part (iv) of Theorem 8.30)
ŒH; Y1 ; : : : ; Yi H; Y1 ; : : : ; Yi
1 ; H .Xi /; Xi C1 ; : : : ; Xn I 1;
Yi ; Xi C1 ; : : : ; Xn
(by Theorem 8.32) D
n X
ŒH; H .Xi /I H; Yi
i D1 p 1 n mˇ 2
C˛
(by Theorem 8.33)
(by Theorem 8.37):
Another source of “low quality” randomness arises in certain cryptographic applications, where we have a “secret value” X , which is a random variable that takes values in a set S, and which has small collision or guessing probability. We want to derive from X a “secret key” whose distance from uniform on some specified “key space” T is small. Typically, T is the set of all bit strings of some specified length, as in Example 8.25. Theorem 8.38, allows us to do this using a “public” hash function — generated at random once and for all, published for all to see, and used over and over to derive secret keys as needed. However, to apply this theorem, it is crucial that the secret values (and the hash key) are mutually independent. E XERCISE 8.64. Consider again the situation in Theorem 8.37. Suppose that T D f0; : : : ; m 1g, but that we would rather have a nearly uniform distribution over T 0 D f0; : : : ; m0 1g, for some m0 < m. While it may be possible to work with a different family of hash functions, we do not have to if m is large enough with respect to m0 , in which case we can just use the value Y 0 WD H p .X / mod m0 . Show that the distance of .H; Y 0 / from uniform on RT 0 is at most 21 mˇ C ˛Cm0 =m. E XERCISE 8.65. Let f r gr2R be a .1 C ˛/=m-almost universal family of hash functions from S to T , where m WD jT j. Suppose H; X ; Y ; Z are random variables, where H is uniformly distributed over R, X takes values in S, Y is uniformly distributed over T , and U is the set of values taken by Z with non-zero probability. Assume that the family of random variables H, Y , .X ; Z / is mutually independent. P 2 (a) For u 2 U , define ˇ.u/ WD s2S PŒX D s j Z D u . Also, let P 0 ˇ pWD u2U ˇ.u/PŒZ D u. Show that ŒH; H .X /; Z I H; Y ; Z 1 0 2 mˇ C ˛.
8.10 Discrete probability distributions
269
(b) Suppose that X is uniformly distributed over a subset S 0 of S , and that Z D f .X / for some pfunction f W S ! U . Show that ŒH; H .X /; Z I H; Y ; Z 12 mjU j=jS 0 j C ˛. 8.10 Discrete probability distributions In addition to working with probability distributions over finite sample spaces, one can also work with distributions over infinite sample spaces. If the sample space is countable, that is, either finite or countably infinite (see §A3), then the distribution is called a discrete probability distribution. We shall not consider any other types of probability distributions in this text. The theory developed in §§8.1–8.5 extends fairly easily to the countably infinite setting, and in this section, we discuss how this is done. 8.10.1 Basic definitions To say that the sample space ˝ is countably infinite simply means that there is a bijection f from the set of positive integers onto ˝; thus, we can enumerate the elements of ˝ as !1 ; !2 ; !3 ; : : : ; where !i WD f .i /. As in the finite case, a probability distribution on ˝ is a function P W ˝ ! Œ0; 1, where all the probabilities sum to 1, which means that the infinite series P1 iD1 P.!i / converges to one. Luckily, the convergence properties of an infinite series whose terms are all non-negative is invariant under a reordering of terms (see §A6), so it does not matter how we enumerate the elements of ˝. Example 8.43. Suppose we toss a fair coin repeatedly until it comes up heads, and let k be the total number of tosses. We can model this experiment as a discrete probability distribution P, where the sample space consists of the set of all positive integers: for each positive integer k, P.k/ WD 2 k . We can check that indeed P1 k D 1, as required. kD1 2 One may be tempted to model this experiment by setting up a probability distribution on the sample space of all infinite sequences of coin tosses; however, this sample space is not countably infinite, and so we cannot construct a discrete probability distribution on this space. While it is possible to extend the notion of a probability distribution to such spaces, this would take us too far afield. Example 8.44. More generally, suppose we repeatedly execute a Bernoulli trial until it succeeds, where each execution succeeds with probability p > 0 independently of the previous trials, and let k be the total number of trials executed. Then we associate the probability P.k/ WD q k 1 p with each positive integer k, where q WD 1 p, since we have k 1 failures before the one success. One can easily
270
Finite and discrete probability distributions
check that these probabilities sum to 1. Such a distribution is called a geometric distribution. P 3 Example 8.45. The series 1 kD1 1=k converges to some positive number c. Therefore, we can define a probability distribution on the set of positive integers, where we associate with each k 1 the probability 1=ck 3 . As in the finite case, an event is an arbitrary subset A of ˝. The probability PŒA of A is defined as the sum of the probabilities associated with the elements of A. This sum is treated as an infinite series when A is infinite. This series is guaranteed to converge, and its value does not depend on the particular enumeration of the elements of A. Example 8.46. Consider the geometric distribution discussed in Example 8.44, where p is the success probability of each Bernoulli trial, and q WD 1 p. For a given integer i 1, consider the event A that the number of trials executed is at least i . Formally, A is the set of all integers greater than or equal to i . Intuitively, PŒA should be q i 1 , since we perform at least i trials if and only if the first i 1 trials fail. Just to be sure, we can compute X X X 1 PŒA D P.k/ D qk 1p D qi 1p qk D qi 1p D qi 1: 1 q ki
ki
k0
It is an easy matter to check that all the statements and theorems in §8.1 carry over verbatim to the case of countably infinite sample spaces. Moreover, Boole’s inequality (8.6) and equality (8.7) are also valid for countably infinite families of events: Theorem 8.39. Let fAi g1 i D1 be an infinite sequence of events, and let A WD S1 A . i D1 i P (i) PŒA 1 i D1 PŒAi , and P1 (ii) PŒA D i D1 PŒAi , if fAi g1 iD1 is pairwise disjoint. Proof. As in the proof of Theorem 8.1, for ! 2 ˝ and B ˝, define ı! ŒB WD 1 if ! 2 B, and ı! ŒB WD 0 if ! … B. First, suppose that fAi g1 i D1 is pairwise disjoint. P Evidently, ı! ŒA D 1 ı ŒA for each ! 2 ˝, and so ! i i D1 PŒA D
D
X
P.!/ı! ŒA D
!2˝ 1 X X i D1 !2˝
X
P.!/
ı! ŒAi
i D1
!2˝
P.!/ı! ŒAi D
1 X
1 X i D1
PŒAi ;
8.10 Discrete probability distributions
271
where we use the fact that we may reverse the order of summation in an infinite double summation of non-negative terms (see §A7). That proves (ii), and (i) folSi 1 0 lows from (ii), applied to the sequence fA0i g1 j D1 Ai , as i D1 , where Ai WD Ai n P P1 1 PŒA D i D1 PŒA0i i D1 PŒAi : 8.10.2 Conditional probability and independence All of the definitions and results in §8.2 carry over verbatim to the countably infinite case. The law of total probability (equations (8.9) and (8.10)), as well as Bayes’ theorem (8.11), extend to families of events fBi gi 2I indexed by any countably infinite set I . The definitions of independent families of events (k-wise and mutually) extend verbatim to infinite families. 8.10.3 Random variables All of the definitions and results in §8.3 carry over verbatim to the countably infinite case. Note that the image of a random variable may be either finite or countably infinite. The definitions of independent families of random variables (k-wise and mutually) extend verbatim to infinite families. 8.10.4 Expectation and variance We define the expected value of a real-valued random variable X exactly as in P (8.18); that is, EŒX WD ! X .!/P.!/, but where this sum is now an infinite series. If this series converges absolutely (see §A6), then we say that X has finite expectation, or that EŒX is finite. In this case, the series defining EŒX converges to the same finite limit, regardless of the ordering of the terms. If EŒX is not finite, then under the right conditions, EŒX may still exist, although its value will be ˙1. Consider first the case where X takes only nonnegative values. In this case, if EŒX is not finite, then we naturally define EŒX WD 1, as the series defining EŒX diverges to 1, regardless of the ordering of the terms. In the general case, we may define random variables X C and X , where X C .!/ WD maxf0; X .!/g and X .!/ WD maxf0; X .!/g, so that X D X C X , and both X C and X take only non-negative values. Clearly, X has finite expectation if and only if both X C and X have finite expectation. Now suppose that EŒX is not finite, so that one of EŒX C or EŒX is infinite. If EŒX C D EŒX D 1, then we say that EŒX does not exist; otherwise, we define EŒX WD EŒX C EŒX , which is ˙1; in this case, the series defining EŒX diverges to ˙1, regardless of the ordering of the terms.
272
Finite and discrete probability distributions
Example 8.47. Let X be a random variable whose distribution is as in ExamP P1 2 ple 8.45. Since the series 1 kD1 1=k converges and the series kD1 1=k di2 verges, the expectation EŒX is finite, while EŒX D 1. One may also verify that the random variable . 1/X X 2 has no expectation. All of the results in §8.4 carry over essentially unchanged, although one must pay some attention to “convergence issues.” P If EŒX exists, then we can regroup the terms in the series ! X .!/P.!/, without affecting its value. In particular, equation (8.19) holds provided EŒX exists, and equation (8.20) holds provided EŒf .X / exists. Theorem 8.14 still holds, under the additional hypothesis that EŒX and EŒY are finite. Equation (8.21) also holds, provided the individual expectations EŒXi are finite. More generally, if EŒX and EŒY exist, then EŒX C Y D EŒX C EŒY , unless EŒX D 1 and EŒY D 1, or EŒX D 1 and EŒY D 1. Also, if EŒX exists, then EŒaX D aEŒX , unless a D 0 and EŒX D ˙1. One might consider generalizing (8.21) to countably infinite families of random variables. To this end, suppose fXi g1 infinite sequence of real-valued i D1 is an P 1 random variables. The random variable X WD i D1 Xi is well defined, proP1 vided the series iD1 Xi .!/ converges for each ! 2 ˝. One might hope that P1 EŒX D i D1 EŒXi ; however, this is not in general true, even if the individual expectations, EŒXi , are non-negative, and even if the series defining X converges absolutely for each !; nevertheless, it is true when the Xi ’s are non-negative: Theorem 8.40. Let fXi g1 i D1 be an infinite sequence of random variables. Suppose that for each i 1, Xi takes non-negative values only, and has finite exP pectation. Also suppose that 1 i D1 Xi .!/ converges for each ! 2 ˝, and define P1 X WD i D1 Xi . Then we have EŒX D
1 X
EŒXi :
iD1
Proof. This is a calculation just like the one made in the proof of Theorem 8.39, where again, we use the fact that we may reverse the order of summation in an infinite double summation of non-negative terms: EŒX D
D
X
P.!/X .!/ D
!2˝ 1 X X i D1 !2˝
X
P.!/
Xi .!/
i D1
!2˝
P.!/Xi .!/ D
1 X
1 X
EŒXi :
i D1
Theorem 8.15 holds under the additional hypothesis that EŒX and EŒY are fi-
273
8.10 Discrete probability distributions
nite. Equation (8.22) also holds, provided the individual expectations EŒXi are finite. Theorem 8.16 still holds, of course. Theorem 8.17 also holds, but where now the sum may be infinite; it can be proved using essentially the same argument as in the finite case, combined with Theorem 8.40. Example 8.48. Suppose X is a random variable with a geometric distribution, as in Example 8.44, with an associated success probability p and failure probability q WD 1 p. As we saw in Example 8.46, for every integer i 1, we have PŒX i D q i 1 . We may therefore apply the infinite version of Theorem 8.17 to easily compute the expected value of X : EŒX D
1 X i D1
PŒX i D
1 X i D1
qi
1
D
1 1
q
D
1 : p
Example 8.49. To illustrate that Theorem 8.40 does not hold in general, consider the geometric distribution on the positive integers, where P.j / D 2 j for j 1. For i 1, define the random variable Xi so that Xi .i / D 2i , Xi .i C 1/ D 2i C1 , and Xi .j / D 0 for all j … fi; i C 1g. Then EŒXi D 0 for all i 1, and so P P i 1 EŒXi D 0. Now define X WD i 1 Xi . This is well defined, and in fact X .1/ D 2, while X .j / D 0 for all j > 1. Hence EŒX D 1. The variance VarŒX of X exists only when WD EŒX is finite, in which case it is defined as usual as EŒ.X /2 , which may be either finite or infinite. Theorems 8.18, 8.19, and 8.20 hold provided all the relevant expectations and variances are finite. The definition of conditional expectation carries over verbatim. Equation (8.23) holds, provided EŒX j B exists, and equation (8.24) holds, provided EŒX exists. Equation (8.24) also holds for a countably infinite partition fBi gi 2I , provided EŒX exists, and each of the conditional expectations EŒX j Bi is finite. 8.10.5 Some useful bounds All of the results in this section hold, provided the relevant expectations and variances are finite. E XERCISE 8.66. Let fAi g1 be a family of events, such that Ai Ai C1 for Si D1 each i 1, and let A WD 1 A i D1 i . Show that PŒA D limi !1 PŒAi . E XERCISE 8.67. Generalize Exercises 8.6 and 8.19 to the discrete setting, allowing a countably infinite index set I . E XERCISE 8.68. Suppose X is a random variable taking positive integer values,
274
Finite and discrete probability distributions
and that for some real number q, with 0 q 1, and for all integers i 1, we have PŒX i D q i 1 . Show that X has a geometric distribution with associated success probability p WD 1 q. E XERCISE 8.69. This exercise extends Jensen’s inequality (see Exercise 8.20) to the discrete setting. Suppose that f is a convex function on an interval I . Let X be a random variable whose image is a countably infinite subset of I , and assume that both EŒX and EŒf .X / are finite. Show that EŒf .X / f .EŒX /. Hint: use continuity. E XERCISE 8.70. A gambler plays a simple game in a casino: with each play of the game, the gambler may bet any number m of dollars; a fair coin is tossed, and if it comes up heads, the casino pays m dollars to the gambler, and otherwise, the gambler pays m dollars to the casino. The gambler plays the game repeatedly, using the following strategy: he initially bets a dollar, and with each subsequent play, he doubles his bet; if he ever wins, he quits and goes home; if he runs out of money, he also goes home; otherwise, he plays again. Show that if the gambler has an infinite amount of money, then his expected winnings are one dollar, and if he has a finite amount of money, his expected winnings are zero. 8.11 Notes The idea of sharing a secret via polynomial evaluation and interpolation (see Example 8.28) is due to Shamir [88]. Our Chernoff bound (Theorem 8.24) is one of a number of different types of bounds that appear in the literature under the rubric of “Chernoff bound.” Universal and pairwise independent hash functions, with applications to hash tables and message authentication codes, were introduced by Carter and Wegman [25, 103]. The notions of -almost universal and -almost strongly universal hashing were developed by Stinson [99]. The notion of -variationally universal hashing (see Exercise 8.57) is from Krovetz and Rogaway [56]. The leftover hash lemma (Theorem 8.37) was originally stated and proved by Impagliazzo, Levin, and Luby [47], who use it to obtain an important result in the theory of cryptography. Our proof of the leftover hash lemma is loosely based on one by Impagliazzo and Zuckermann [48], who also present further applications.
9 Probabilistic algorithms
It is sometimes useful to endow our algorithms with the ability to generate random numbers. In fact, we have already seen two examples of how such probabilistic algorithms may be useful: at the end of §3.4, we saw how a probabilistic algorithm might be used to build a simple and efficient primality test; however, this test might incorrectly assert that a composite number is prime; in the next chapter, we will see how a small modification to this algorithm will ensure that the probability of making such a mistake is extremely small; in §4.5, we saw how a probabilistic algorithm could be used to make Fermat’s two squares theorem constructive; in this case, the use of randomization never leads to incorrect results, but the running time of the algorithm was only bounded “in expectation.” We will see a number of other probabilistic algorithms in this text, and it is high time that we place them on a firm theoretical foundation. To simplify matters, we only consider algorithms that generate random bits. Where such random bits actually come from will not be of great concern to us here. In a practical implementation, one would use a pseudo-random bit generator, which should produce bits that “for all practical purposes” are “as good as random.” While there is a well-developed theory of pseudo-random bit generation (some of which builds on the ideas in §8.9), we will not delve into this here. Moreover, the pseudo-random bit generators used in practice are not based on this general theory, and are much more ad hoc in design. So, although we will present a rigorous formal theory of probabilistic algorithms, the application of this theory to practice is ultimately a bit heuristic; nevertheless, experience with these algorithms has shown that the theory is a very good predictor of the real-world behavior of these algorithms.
275
276
Probabilistic algorithms
9.1 Basic definitions Formally speaking, we will add a new type of instruction to our random access machine (described in §3.2): random bit This type of instruction is of the form RAND, where takes the same form as in arithmetic instructions. Execution of this type of instruction assigns to a value sampled from the uniform distribution on f0; 1g, independently from the execution of all other random-bit instructions. Algorithms that use random-bit instructions are called probabilistic (or randomized), while those that do not are called called deterministic. In describing probabilistic algorithms at a high level, we shall write “y R f0; 1g” to denote the assignment of a random bit to the variable y, and “y R f0; 1g` ” to denote the assignment of a random bit string of length ` to the variable y. To analyze the behavior of a probabilistic algorithm, we first need a probability distribution that appropriately models its execution. Once we have done this, we shall define the running time and output to be random variables associated with this distribution. 9.1.1 Defining the distribution It would be desirable to define a probability distribution that could be used for all algorithms and all inputs. While this can be done in principle, it would require notions from the theory of probability more advanced than those we developed in the previous chapter. Instead, for a given probabilistic algorithm A and input x, we shall define a discrete probability distribution that models A’s execution on input x. Thus, every algorithm/input pair yields a different distribution. To motivate our definition, consider Example 8.43. We could view the sample space in that example to be the set of all bit strings consisting of zero or more 0 bits, followed by a single 1 bit, and to each such bit string ! of this special form, we assign the probability 2 j!j , where j!j denotes the length of !. The “random experiment” we have in mind is to generate random bits one at a time until one of these special “halting” strings is generated. In developing the definition of the probability distribution for a probabilistic algorithm, we simply consider more general sets of “halting” strings, as determined by the algorithm and its input. So consider a fixed algorithm A and input x. Let be a finite bit string of length, say, `. We can use to “drive” the execution of A on input x for up to ` execution steps, as follows: for each step i D 1; : : : ; `, if the i th instruction executed by A is RAND, the i th bit of is assigned to . In this context, we shall refer to as an execution path. The reader may wish to visualize as a finite path in an
277
9.1 Basic definitions
infinite binary tree, where we start at the root, branching to the left if the next bit in is a 0 bit, and branching to the right if the next bit in is a 1 bit. After using to drive A on input x for up to ` steps, we might find that the algorithm executed a halt instruction at some point during the execution, in which case we call a complete execution path; moreover, if this halt instruction was the `th instruction executed by A, then we call an exact execution path. Our intent is to define the probability distribution associated with A on input x to be P W ˝ ! Œ0; 1, where the sample space ˝ is the set of all exact execution paths, and P.!/ WD 2 j!j for each ! 2 ˝. However, for this to work, all the probabilities must sum to 1. The next theorem at least guarantees that these probabilities sum to at most 1. The only property of ˝ that really matters in the proof of this theorem is that it is prefix free, which means that no exact execution path is a proper prefix of any other. Theorem 9.1. Let ˝ be the set of all exact execution paths for A on input x. Then P j!j 1. !2˝ 2 Proof. Let k be a non-negative integer. Let ˝k ˝ be the set of all exact execuP tion paths of length at most k, and let ˛k WD !2˝k 2 j!j . We shall show below that ˛k 1: From this, it will follow that X
2
j!j
(9.1)
D lim ˛k 1:
!2˝
k!1
To prove the inequality (9.1), consider the set Ck of all complete execution paths of length equal to k. We claim that ˛k D 2
k
jCk j;
(9.2)
from which (9.1) follows, since clearly, jCk j 2k . So now we are left to prove (9.2). Observe that by definition, each 2 Ck extends some ! 2 ˝k ; that is, ! is a prefix of ; moreover, ! is uniquely determined by , since no exact execution path is a proper prefix of any other exact execution path. Also observe that for each ! 2 ˝k , if Ck .!/ is the set of execution paths 2 Ck that extend !, then jCk .!/j D 2k j!j , and by the previous observation, fCk .!/g!2˝k is a partition of Ck . Thus, we have X X X X X ˛k D 2 j!j D 2 j!j 2 kCj!j D 2 k 1 D 2 k jCk j; !2˝k
!2˝k
which proves (9.2).
2Ck .!/
!2˝k 2Ck .!/
278
Probabilistic algorithms
From the above theorem, if ˝ is the set of all exact execution paths for A on input x, then X ˛ WD 2 j!j 1; !2˝
and we say that A halts with probability ˛ on input x. If ˛ D 1, we define the distribution P W ˝ ! Œ0; 1 associated with A on input x, where P.!/ WD 2 j!j for each ! 2 ˝. We shall mainly be interested in algorithms that halt with probability 1 on all inputs. The following four examples provide some simple criteria that guarantee this. Example 9.1. Suppose that on input x, A always halts within a finite number of steps, regardless of its random choices. More precisely, this means that there is a bound ` (depending on A and x), such that all execution paths of length ` are complete. In this case, we say that A’s running time on input x is strictly bounded by `, and it is clear that A halts with probability 1 on input x. Moreover, one can much more simply model A’s computation on input x by working with the uniform distribution on execution paths of length `. Example 9.2. Suppose A and B are probabilistic algorithms that both halt with probability 1 on all inputs. Using A and B as subroutines, we can form their serial composition; that is, we can construct the algorithm C.x/ W
output B.A.x//,
which on input x, first runs A on input x, obtaining a value y, then runs B on input y, obtaining a value z, and finally, outputs z. We claim that C halts with probability 1 on all inputs. For simplicity, we may assume that A places its output y in a location in memory where B expects to find its input, and that B places its output in a location in memory where C ’s output should go. With these assumptions, the program for C is obtained by simply concatenating the programs for A and B, making the following adjustments: every halt instruction in A’s program is translated into an instruction that branches to the first instruction of B’s program, and every target in a branch instruction in B’s program is increased by the length of A’s program. Let ˝ be the sample space representing A’s execution on an input x. Each ! 2 ˝ determines an output y, and a corresponding sample space ˝!0 representing B’s execution on input y. The sample space representing C ’s execution on input x is ˝ 00 D f!! 0 W ! 2 ˝; ! 0 2 ˝!0 g;
279
9.1 Basic definitions
where !! 0 is the concatenation of ! and ! 0 . We have X X X X 0 0 2 j!! j D 2 j!j 2 j! j D 2 !! 0 2˝ 00
!2˝
0 ! 0 2˝!
j!j
1 D 1;
!2˝
which shows that C halts with probability 1 on input x. Example 9.3. Suppose A, B, and C are probabilistic algorithms that halt with probability 1 on all inputs, and that A always outputs either true or false. Then we can form the conditional construct D.x/ W
if A.x/ then output B.x/ else output C.x/.
By a calculation similar to that in the previous example, it is easy to see that D halts with probability 1 on all inputs. Example 9.4. Suppose A and B are probabilistic algorithms that halt with probability 1 on all inputs, and that A always outputs either true or false. We can form the iterative construct C.x/ W
while A.x/ do x output x.
B.x/
Algorithm C may or may not halt with probability 1. To analyze C , we define an infinite sequence of algorithms fCn g1 nD0 ; namely, we define C0 as C0 .x/ W
halt,
and for n > 0, we define Cn as Cn .x/ W
if A.x/ then Cn
1 .B.x//.
Essentially, Cn drives C for up to n loop iterations before halting, if necessary, in C0 . By the previous three examples, it follows by induction on n that each Cn halts with probability 1 on all inputs. Therefore, we have a well-defined probability distribution for each Cn and each input x. Consider a fixed input x. For each n 0, let ˇn be the probability that on input x, Cn terminates by executing algorithm C0 . Intuitively, ˇn is the probability that C executes at least n loop iterations; however, this probability is defined with respect to the probability distribution associated with algorithm Cn on input x. It is not hard to see that the sequence fˇn g1 nD0 is non-increasing, and so the limit ˇ WD limn!1 ˇn exists; moreover, C halts with probability 1 ˇ on input x. On the one hand, if the loop in algorithm C is guaranteed to terminate after a finite number of iterations (as in a “for loop”), then C certainly halts with probability 1. Indeed, if on input x, there is a bound ` (depending on x) such that the number of loop iterations is always at most `, then ˇ`C1 D ˇ`C2 D D 0. On the other hand, if on input x, C enters into a good, old-fashioned infinite loop, then
280
Probabilistic algorithms
C certainly does not halt with probability 1, as ˇ0 D ˇ1 D D 1. Of course, there may be in-between cases, which require further analysis. We now illustrate the above criteria with a couple of some simple, concrete examples. Example 9.5. Consider the following algorithm, which models an experiment in which we toss a fair coin repeatedly until it comes up heads: repeat b R f0; 1g until b D 1 For each positive integer n, let ˇn be the probability that the algorithm executes at least n loop iterations, in the sense of Example 9.4. It is not hard to see that ˇn D 2 nC1 , and since ˇn ! 0 as n ! 1, the algorithm halts with probability 1, even though the loop is not guaranteed to terminate after any particular, finite number of steps. Example 9.6. Consider the following algorithm: i 0 repeat i i C1 R f0; 1gi until D 0i For each positive integer n, let ˇn be the probability that the algorithm executes at least n loop iterations, in the sense of Example 9.4. It is not hard to see that ˇn D
nY1
.1
i D1
2 i/
nY1
e
2
iC1
De
Pn
2 iD0
2
i
e
2
;
i D1
where we have made use of the estimate (iii) in §A1. Therefore, lim ˇn e
n!1
2
> 0;
and so the algorithm does not halt with probability 1, even though it never falls into an infinite loop. 9.1.2 Defining the running time and output Let A be a probabilistic algorithm that halts with probability 1 on a fixed input x. We may define the random variable Z that represents A’s running time on input x, and the random variable Y that represents A’s output on input x.
9.1 Basic definitions
281
Formally, Z and Y are defined using the probability distribution on the sample space ˝, defined in §9.1.2. The sample space ˝ consists of all exact execution paths for A on input x. For each ! 2 ˝, Z .!/ WD j!j, and Y .!/ is the output produced by A on input x, using ! to drive its execution. The expected running time of A on input x is defined to be EŒZ . Note that in defining the expected running time, we view the input as fixed, rather than drawn from some probability distribution. Also note that the expected running time may be infinite. We say that A runs in expected polynomial time if there exist constants a, b, and c, such that for all n, and for all inputs x of size n, the expected running time of A on input x is at most anb C c. We say that A runs in strict polynomial time if there exist constants a, b, and c, such that for all n, and for all inputs x of size n, A’s running time on input x is strictly bounded by anb C c (as in Example 9.1). Example 9.7. Consider again the algorithm in Example 9.5. Let L be the random variable that represents the number of loop iterations executed by the algorithm. The distribution of L is a geometric distribution, with associated success probability 1=2 (see Example 8.44). Therefore, EŒL D 2 (see Example 8.46). Let Z be the random variable that represents the running time of the algorithm. We have Z cL, for some implementation-dependent constant c. Therefore, EŒZ c EŒL D 2c. Example 9.8. Consider the following probabilistic algorithm that takes as input a positive integer m. It models an experiment in which we toss a fair coin repeatedly until it comes up heads m times. k 0 repeat b R f0; 1g if b D 1 then k until k D m
kC1
Let L be the random variable that represents the number of loop iterations executed the algorithm on a fixed input m. We claim that EŒL D 2m. To see this, define random variables L1 ; : : : ; Lm , where L1 is the number of loop iterations needed to get b D 1 for the first time, L2 is the number of additional loop iterations additional loop iterations needed to bet b D 1 for the second time, and so on. Clearly, we have L D L1 C C Lm , and moreover, EŒLi D 2 for i D 1; : : : ; m; therefore, by linearity of expectation, we have EŒL D EŒL1 C C EŒLm D 2m. It follows that the expected running time of this algorithm on input m is O.m/.
282
Probabilistic algorithms
Example 9.9. Consider the following algorithm: n 0 repeat n repeat
n C 1, b R f0; 1g until b D 1 n until D 0n R f0; 1g
The expected running time is infinite (even though it does halt with probability 1). To see this, define random variables L1 and L2 , where L1 is the number of iterations of the first loop, and L2 is the number of iterations of the second. As in Example 9.7, the distribution of L1 is a geometric distribution with associated success probability 1=2, and EŒL1 D 2. For each k 1, the conditional distribution of L2 given L1 D k is a geometric distribution with associated success probability 1=2k , and so EŒL2 j L1 D k D 2k . Therefore, X X X EŒL2 D EŒL2 j L1 D kPŒL1 D k D 2k 2 k D 1 D 1: k1
k1
k1
We have presented a fairly rigorous definitional framework for probabilistic algorithms, but from now on, we shall generally reason about such algorithms at a higher, and more intuitive, level. Nevertheless, all of our arguments can be translated into this rigorous framework, the details of which we leave to the interested reader. Moreover, all of the algorithms we shall present halt with probability 1 on all inputs, but we shall not go into the details of proving this (but the criteria in Examples 9.1–9.4 can be used to easily verify this). E XERCISE 9.1. Suppose A is a probabilistic algorithm that halts with probability 1 on input x, and let P W ˝ ! Œ0; 1 be the corresponding probability distribution. Let be an execution path of length `, and assume that no proper prefix of is exact. Let E WD f! 2 ˝ W ! extends g. Show that PŒE D 2 ` . E XERCISE 9.2. Let A be a probabilistic algorithm that on a given input x, halts with probability 1, and produces an output in the set T . Let P be the corresponding probability distribution, and let Y and Z be random variables representing the output and running time, respectively. For each k 0, let Pk be the uniform distribution on all execution paths of length k. We define random variables Yk and Zk , associated with Pk , as follows: if is complete, we define Yk ./ to be the output produced by A, and Zk ./ to be the actual number of steps executed by A; otherwise, we define Yk ./ to be the special value “?” and Zk ./ to be k. For each t 2 T , let p t k be the probability (relative to Pk ) that Yk D t, and let k be the expected value (relative to Pk ) of Zk . Show that: (a) for each t 2 T , PŒY D t D lim p t k ; k!1
9.2 Generating a random number from a given interval
283
(b) EŒZ D lim k . k!1
E XERCISE 9.3. Let A1 and A2 be probabilistic algorithms. Let B be any probabilistic algorithm that always outputs 0 or 1. For for i D 1; 2, let A0i be the algorithm that on input x computes and outputs B.Ai .x//. Fix an input x, and let Y1 and Y2 be random variables representing the outputs of A1 and A2 , respectively, on input x, and let Y10 and Y20 be random variables representing the outputs of A01 and A02 , respectively, on input x. Assume that the images of Y1 and Y2 are finite, and let ı WD ŒY1 I Y2 be their statistical distance. Show that jPŒY10 D 1 PŒY20 D 1j ı. 9.2 Generating a random number from a given interval Suppose we want to generate a number, uniformly at random from the interval f0; : : : ; m 1g, for a given positive integer m. If m is a power of 2, say m D 2` , then we can do this directly as follows: generate a random `-bit string , and convert to the integer I. / whose base-2 representation is ; that is, if D b` 1 b` 2 b0 , where the bi ’s are bits, then I. / WD
` 1 X
bi 2i :
i D0
In the general case, we do not have a direct way to do this, since we can only directly generate random bits. But the following algorithm does the job: Algorithm RN. On input m, where m is a positive integer, do the following, where ` WD dlog2 me: repeat R f0; 1g` y I. / until y < m output y Theorem 9.2. On input m, the expected running time of Algorithm RN is O.len.m//, and its output is uniformly distributed over f0; : : : ; m 1g. Proof. Note that m 2` < 2m. Let L denote the number of loop iterations of this algorithm, and Z its running time. With every loop iteration, the algorithm halts with probability m=2` , and so the distribution of L is a geometric distribution with associated success probability m=2` > 1=2. Therefore, EŒL D 2` =m < 2. Since Z c len.m/ L for some constant c, it follows that EŒZ D O.len.m//.
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Probabilistic algorithms
Next, we analyze the output distribution. Let Y denote the output of the algorithm. We want to show that Y is uniformly distributed over f0; : : : ; m 1g. This is perhaps intuitively obvious, but let us give a rigorous justification of this claim. To do this, for i D 1; 2; : : : ; let Yi denote the value of y in the i th loop iteration; for completeness, if the i th loop iteration is not executed, then we define Yi WD ?. Also, for i D 1; 2 : : : ; let Hi be the event that the algorithm halts in the i th loop iteration (i.e., Hi is the event that L D i). Let t 2 f0; : : : ; m 1g be fixed. First, by total probability (specifically, the infinite version of (8.9), discussed in §8.10.2), we have X X PŒ.Y D t / \ Hi D PŒ.Yi D t / \ Hi : (9.3) PŒY D t D i 1
i 1
Next, observe that as each loop iteration works the same as any other, it follows that for each i 1, we have PŒ.Yi D t/ \ Hi j L i D PŒ.Y1 D t / \ H1 D PŒY1 D t D 2
`
:
Moreover, since Hi implies L i , we have PŒ.Yi D t/ \ Hi D PŒ.Yi D t / \ Hi \ .L i / `
D PŒ.Yi D t / \ Hi j L i PŒL i D 2
PŒL i ;
and so using (9.3) and the infinite version of Theorem 8.17 (discussed in §8.10.4), we have X X X PŒY D t D PŒ.Yi D t / \ Hi D 2 ` PŒL i D 2 ` PŒL i i 1
D2
`
i 1
EŒL D 2
`
i 1
`
2 =m D 1=m:
This shows that Y is uniformly distributed over f0; : : : ; m
1g.
Of course, by adding an appropriate value to the output of Algorithm RN, we can generate random numbers uniformly in the interval fm1 ; : : : ; m2 g, for any given m1 and m2 . In what follows, we shall denote the execution of this algorithm as y
R
fm1 ; : : : ; m2 g:
More generally, if T is any finite, non-empty set for which we have an efficient algorithm whose output is uniformly distributed over T , we shall denote the execution of this algorithm as y
R
T:
For example, we may write y
R
Zm
9.3 The generate and test paradigm
285
to denote assignment to y of a randomly chosen element of Zm . Of course, this is done by running Algorithm RN on input m, and viewing its output as a residue class modulo m. We also mention the following alternative algorithm for generating an almostrandom number from an interval. Algorithm RN 0 . On input m; k, where both m and k are positive integers, do the following, where ` WD dlog2 me: R f0; 1g.`Ck/ y I./ mod m output y Compared with Algorithm RN, Algorithm RN 0 has the advantage that there are no loops—it always halts in a bounded number of steps; however, it has the disadvantage that its output is not uniformly distributed over the interval f0; : : : ; m 1g. Nevertheless, the statistical distance between its output distribution and the uniform distribution on f0; : : : ; m 1g is at most 2 k (see Example 8.41 in §8.8). Thus, by choosing k suitably large, we can make the output distribution “as good as uniform” for most practical purposes. E XERCISE 9.4. Prove that no probabilistic algorithm whose running time is strictly bounded can have an output distribution that is uniform on f0; : : : ; m 1g, unless m is a power of 2. E XERCISE 9.5. You are to design and analyze an efficient probabilistic algorithm B that takes as input two integers n and y, with n > 0 and 0 y n, and always outputs 0 or 1. Your algorithm should satisfy the following property. Suppose A is a probabilistic algorithm that takes two inputs, n and x, and always outputs an integer between 0 and n. Let Y be a random variable representing A’s output on input n; x. Then for all inputs n; x, we should have PŒB.n; A.n; x// outputs 1 D EŒY =n: 9.3 The generate and test paradigm Algorithm RN, which was discussed in §9.2, is a specific instance of a very general type of construction that may be called the “generate and test” paradigm. Suppose we have two probabilistic algorithms, A and B, and we combine them to form a new algorithm C.x/ W
repeat y output y.
A.x/ until B.x; y/
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Probabilistic algorithms
Here, we assume that B.x; y/ outputs either true or false. Our goal is to answer the following questions about C for a fixed input x: 1. Does C halt with probability 1? 2. What is the expected running time of C ? 3. What is the output distribution of C ? The answer to the first question is “yes,” provided (i) A halts with probability 1 on input x, (ii) for all possible outputs y of A.x/, B halts with probability 1 on input .x; y/, and (iii) for some possible output y of A.x/, B.x; y/ outputs true with non-zero probability. We shall assume this from now on. To address the second and third questions, let us define random variables L, Z , and Y , where L is the total number of loop iterations of C , Z is the total running time of C , and Y is the output of C . We can reduce the study of L, Z , and Y to the study of a single iteration of the main loop. Instead of working with a new probability distribution that directly models a single iteration of the loop, it is more convenient to simply study the first iteration of the loop in C . To this end, we define random variables Z1 and Y1 , where Z1 is the running time of the first loop iteration of C , and Y1 is the value assigned to y in the first loop iteration of C . Also, let H1 be the event that the algorithm halts in the first loop iteration, and let T be the set of possible outputs of A.x/. Note that by the assumption in the previous paragraph, PŒH1 > 0. Theorem 9.3. Under the assumptions above, (i) L has a geometric distribution with associated success probability PŒH1 , and in particular, EŒL D 1=PŒH1 ; (ii) EŒZ D EŒZ1 EŒL D EŒZ1 =PŒH1 ; (iii) for every t 2 T , PŒY D t D PŒY1 D t j H1 . Proof. (i) is clear. To prove (ii), for i 1, let Zi be the time spent by the algorithm in the i th P loop iteration, so that Z D i 1 Zi . Now, the conditional distribution of Zi given L i is essentially the same as the distribution of Z1 ; moreover, Zi D 0 when L < i. Therefore, by (8.24), for each i 1, we have EŒZi D EŒZi j L i PŒL i C EŒZi j L < i PŒL < i D EŒZ1 PŒL i :
We may assume that EŒZ1 is finite, as otherwise (ii) is trivially true. By Theorem 8.40 and the infinite version of Theorem 8.17 (discussed in §8.10.4), we have X X X EŒZ D EŒZi D EŒZ1 PŒL i D EŒZ1 PŒL i D EŒZ1 EŒL: i 1
i 1
i 1
To prove (iii), for i 1, let Yi be the value assigned to y in loop iteration i ,
9.3 The generate and test paradigm
287
with Yi WD ? if L < i , and let Hi be the event that the algorithm halts in loop iteration i (i.e., Hi is the event that L D i ). By a calculation similar to that made in the proof of Theorem 9.2, for each t 2 T , we have X X PŒY D t D PŒ.Y D t / \ Hi D PŒ.Yi D t / \ Hi j L i PŒL i i 1
D PŒ.Y1 D t / \ H1
i 1
X
PŒL i D PŒ.Y1 D t / \ H1 EŒL
i 1
D PŒ.Y1 D t / \ H1 =PŒH1 D PŒY1 D t j H1 : Example 9.10. Suppose T is a finite set, and T 0 is a non-empty, finite subset of T . Consider the following generalization of Algorithm RN: repeat y RT until y 2 T 0 output y Here, we assume that we have an algorithm to generate a random element of T (i.e., uniformly distributed over T ), and an efficient algorithm to test for membership in T 0 . Let L denote the number of loop iterations, and Y the output. Also, let Y1 be the value of y in the first iteration, and H1 the event that the algorithm halts in the first iteration. Since Y1 is uniformly distributed over T , and H1 is the event that Y1 2 T 0 , we have PŒH1 D jT 0 j=jT j. It follows that EŒL D jT j=jT 0 j. As for the output, for every t 2 T , we have PŒY D t D PŒY1 D t j H1 D PŒY1 D t j Y1 2 T 0 ;
which is 0 if t … T 0 and is 1=jT 0 j is t 2 T 0 . It follows that Y is uniformly distributed over T 0 . Example 9.11. Let us analyze the following algorithm: repeat y R f1; 2; 3; 4g z R f1; : : : ; yg until z D 1 output y Let L denote the number of loop iterations, and Y the output. Also, let Y1 be the value of y in the first iteration, and H1 the event that the algorithm halts in the first iteration. Y1 is uniformly distributed over f1; : : : ; 4g, and for t D 1; : : : ; 4,
288
Probabilistic algorithms
PŒH1 j Y1 D t D 1=t . Therefore, PŒH1 D
4 X
PŒH1 j Y1 D t PŒY1 D t D
t D1
4 X
.1=t /.1=4/ D 25=48:
t D1
Thus, EŒL D 48=25. For the output distribution, for t D 1; : : : ; 4, we have PŒY D t D PŒY1 D t j H1 D PŒ.Y1 D t / \ H1 =PŒH1
12 : 25t This example illustrates how a probabilistic test can be used to create a biased output distribution. D PŒH1 j Y1 D t PŒY1 D t =PŒH1 D .1=t /.1=4/.48=25/ D
E XERCISE 9.6. Design and analyze an efficient probabilistic algorithm that takes as input an integer n 2, and outputs a random element of Zn . E XERCISE 9.7. Consider the following probabilistic algorithm that takes as input a positive integer m: S ; repeat n R f1; : : : ; mg S S [ fng until jS j D m Show that the expected number of iterations of the main loop is m log m. E XERCISE 9.8. Consider the following algorithm (which takes no input): j 1 repeat j j C1 n R f0; : : : ; j until n D 0
1g
Show the expected running time of this algorithm is infinite (even though it does halt with probability 1). E XERCISE 9.9. Now consider the following modification to the algorithm in the previous exercise:
9.3 The generate and test paradigm
j 2 repeat j j C1 n R f0; : : : ; j until n D 0 or n D 1
289
1g
Show the expected running time of this algorithm is finite. E XERCISE 9.10. Consider again Algorithm RN in §9.2. On input m, this algorithm may use up to 2` random bits on average, where ` WD dlog2 me. Indeed, each loop iteration generates ` random bits, and the expected number of loop iterations will be 2 when m 2` 1 . This exercise asks you to analyze an alternative algorithm that uses just ` C O.1/ random bits on average, which may be useful in settings where random bits are expensive for some reason. This algorithm runs as follows:
./
repeat y 0; i 1 while y < m and i ` do b R f0; 1g; y y C 2` i b; i until y < m output y
i C1
Define random variables K and Y , where K is the number of times the line marked ./ is executed, and Y is the output. Show that EŒK D ` C O.1/ and that Y is uniformly distributed over f0; : : : ; m 1g. E XERCISE 9.11. Let S and T be finite, non-empty sets, and let f W S T ! f 1; 0; 1g be a function. Consider the following probabilistic algorithm:
./
x R S, y R T if f .x; y/ D 0 then y0 y else y0 R T while f .x; y 0 / D 0 do y 0
R
T
Here, we assume we have algorithms to generate random elements in S and T , and a deterministic algorithm to evaluate f . Define random variables X , Y , Y 0 , and L, where X is the value assigned to x, Y is the value assigned to y, Y 0 is the final value assigned to y 0 , and L is the number of times that f is evaluated at the line marked ./. (a) Show that .X ; Y 0 / has the same distribution as .X ; Y /.
290
Probabilistic algorithms
(b) Show that EŒL 1. (c) Give an explicit example of S , T , and f , such that if the line marked ./ is deleted, then EŒf .X ; Y / > EŒf .X ; Y 0 / D 0. 9.4 Generating a random prime Suppose we are given an integer m 2, and want to generate a random prime between 2 and m. One way to proceed is simply to generate random numbers until we get a prime. This idea will work, assuming the existence of an efficient, deterministic algorithm IsPrime that determines whether or not a given integer is prime. We will present such an algorithm later, in Chapter 21. For the moment, we shall just assume we have such an algorithm, and use it as a “black box.” Let us assume that on inputs of bit length at most `, IsPrime runs in time at most .`/. Let us also assume (quite reasonably) that .`/ D .`/. Algorithm RP. On input m, where m is an integer 2, do the following: repeat n R f2; : : : ; mg until IsPrime.n/ output n We now wish to analyze the running time and output distribution of Algorithm RP on an input m, where ` WD len.m/. This is easily done, using the results of §9.3, and more specifically, by Example 9.10. The expected number of loop iterations performed by Algorithm RP is .m 1/=.m/, where .m/ is the number of primes up to m. By Chebyshev’s theorem (Theorem 5.1), .m/ D ‚.m=`/. It follows that the expected number of loop iterations is ‚.`/. Furthermore, the expected running time of any one loop iteration is O. .`// (the expected running time for generating n is O.`/, and this is where we use the assumption that .`/ D .`/). It follows that the expected total running time is O.` .`//. As for the output, it is clear that it is uniformly distributed over the set of primes up to m. 9.4.1 Using a probabilistic primality test In the above analysis, we assumed that IsPrime was an efficient, deterministic algorithm. While such an algorithm exists, there are in fact simpler and far more efficient primality tests that are probabilistic. We shall discuss such an algorithm in detail in the next chapter. This algorithm (like several other probabilistic primality tests) has one-sided error, in the following sense: if the input n is prime, then the algorithm always outputs true; otherwise, if n is composite, the output may be true or false, but the probability that the output is true is at most , where is a
9.4 Generating a random prime
291
very small number (the algorithm may be easily tuned to make quite small, e.g., 2 100 ). Let us analyze the behavior of Algorithm RP under the assumption that IsPrime is implemented by a probabilistic algorithm with an error probability for composite inputs bounded by , as discussed in the previous paragraph. Let N .`/ be a bound on the expected running time of this algorithm for all inputs of bit length at most `. Again, we assume that N .`/ D .`/. We use the technique developed in §9.3. Consider a fixed input m with ` WD len.m/. Let L, Z , and N be random variables representing, respectively, the number of loop iterations, the total running time, and output of Algorithm RP on input m. Also, let Z1 be the random variable representing the running time of the first loop iteration, and let N1 be the random variable representing the value assigned to n in the first loop iteration. Let H1 be the event that the algorithm halts in the first loop iteration, and let C1 be the event that N1 is composite. Clearly, N1 is uniformly distributed over f2; : : : ; mg. Also, by our assumptions about IsPrime, we have EŒZ1 D O. N .`//;
and moreover, for each j 2 f2; : : : ; mg, we have PŒH1 j N1 D j if j is composite,
and PŒH1 j N1 D j D 1 if j is prime.
In particular, PŒH1 j C1 and PŒH1 j Cx1 D 1:
It follows that PŒH1 D PŒH1 j C1 PŒC1 C PŒH1 j Cx1 PŒCx1 PŒH1 j Cx1 PŒCx1
D .m/=.m
1/:
Therefore, EŒL .m
1/=.m/ D O.`/
and EŒZ D EŒLEŒZ1 D O.` N .`//:
That takes care of the running time. Now consider the output. For every j 2 f2; : : : ; mg, we have PŒN D j D PŒN1 D j j H1 :
292
Probabilistic algorithms
If j is prime, then PŒN D j D PŒN1 D j j H1 D
D
PŒ.N1 D j / \ H1 PŒH1
PŒH1 j N1 D j PŒN1 D j PŒH1
D
.m
1 : 1/PŒH1
Thus, every prime is output with equal probability; however, the algorithm may also output a number that is not prime. Let us bound the probability of this event. One might be tempted to say that this happens with probability at most ; however, in drawing such a conclusion, one would be committing the fallacy of Example 8.13 — to correctly analyze the probability that Algorithm RP mistakenly outputs a composite, one must take into account the rate of incidence of the “primality disease,” as well as the error rate of the test for this disease. Indeed, if C is the event that N is composite, then we have PŒC D PŒC1 j H1 D
PŒC1 \ H1
D
PŒH1 j C1 PŒC1
PŒH1 PŒH1 D O.`/: PŒH1 .m/=.m 1/
Another way of analyzing the output distribution of Algorithm RP is to consider its statistical distance from the uniform distribution on the set of primes between 2 and m. As we have already argued, every prime between 2 and m is equally likely to be output, and in particular, any fixed prime is output with probability at most 1=.m/. It follows from Theorem 8.31 that D PŒC D O.`/. 9.4.2 Generating a random `-bit prime Instead of generating a random prime between 2 and m, we may instead want to generate a random `-bit prime, that is, a prime between 2` 1 and 2` 1. Bertrand’s postulate (Theorem 5.8) tells us that there exist such primes for every ` 2, and that in fact, there are .2` =`/ such primes. Because of this, we can modify Algorithm RP, so that each candidate n is chosen at random from the interval f2` 1 ; : : : ; 2` 1g, and all of the results for that algorithm carry over essentially without change. In particular, the expected number of trials until the algorithm halts is O.`/, and if a probabilistic primality test as in §9.4.1 is used, with an error probability of , the probability that the output is not prime is O.`/. E XERCISE 9.12. Suppose Algorithm RP is implemented using an imperfect random number generator, so that the statistical distance between the output distribution of the random number generator and the uniform distribution on f2; : : : ; mg is
9.5 Generating a random non-increasing sequence
293
equal to ı (e.g., Algorithm RN 0 in §9.2). Assume that 2ı < .m/=.m 1/. Also, let denote the expected number of iterations of the main loop of Algorithm RP, let denote the statistical distance between its output distribution and the uniform distribution on the primes up to m, and let ` WD len.m/. (a) Assuming the primality test is deterministic, show that D O.`/ and D O.ı`/. (b) Assuming the primality test is probabilistic, with one-sided error , as in §9.4.1, show that D O.`/ and D O..ı C /`/. 9.5 Generating a random non-increasing sequence The following algorithm will be used in the next section as a fundamental subroutine in a beautiful algorithm (Algorithm RFN) that generates random numbers in factored form. Algorithm RS. On input m, where m is an integer 2, do the following: n0 m k 0 repeat k kC1 nk R f1; : : : ; nk until nk D 1 output .n1 ; : : : ; nk /
1g
We analyze first the output distribution, and then the running time. 9.5.1 Analysis of the output distribution Let N1 ; N2 ; : : : be random variables denoting the choices of n1 ; n2 ; : : : (for completeness, define Ni WD 1 if loop i is never entered). A particular output of the algorithm is a non-increasing sequence .j1 ; : : : ; jh /, where j1 j2 jh 1 > jh D 1. For any such sequence, we have h h\
P
vD1
h i h i Y \ .Nv D jv / D PŒN1 D j1 P N v D jv j .Nw D jw / vD2
1 1 1 D : m j1 jh 1
w
(9.4)
This completely describes the output distribution, in the sense that we have determined the probability with which each non-increasing sequence appears as an output. However, there is another way to characterize the output distribution that
294
Probabilistic algorithms
is significantly more useful. For j D 2; : : : ; m, define the random variable Oj to be the number of occurrences of the integer j in the output sequence. The Oj ’s determine the Ni ’s, and vice versa. Indeed, Om D em ; : : : ; O2 D e2 if and only if the output of the algorithm is the sequence 1; : : : ; m 1; : : : ; ˜ 2; : : : ; 2; 1/: ™œ
. m; : : : ; m; m em times
em
1
e2 times
times
From (9.4), we can therefore directly compute m h\
P
j D2
m i 1 Y 1 .Oj D ej / D : m j ej
(9.5)
j D2
Moreover, we can write 1=m as a telescoping product, m Y 2 2 1 D .1 1 3 2
1 m 1 m D m m m
1=j /;
j D2
and so re-write (9.5) as m h\
P
m i Y .Oj D ej / D j
j D2
ej
.1
1=j /:
(9.6)
j D2
Notice that for j D 2; : : : ; m, X
j
ej
.1
1=j / D 1;
ej 0
and so by (a discrete version of) Theorem 8.7, the family of random variables fOj gjmD2 is mutually independent, and for each j D 2; : : : ; m and each integer ej 0, we have PŒOj D ej D j
ej
.1
1=j /:
(9.7)
In summary, we have shown: that the family fOj gjmD2 is mutually independent, where for each j D 2; : : : ; m, the variable Oj C 1 has a geometric distribution with an associated success probability of 1 1=j . Another, perhaps more intuitive, analysis of the distribution of the Oj ’s runs as follows. Conditioning on the event Om D em ; : : : ; Oj C1 D ej C1 , one sees that the value of Oj is the number of times the value j appears in the sequence Ni ; Ni C1 ; : : : ; where i D em C C ej C1 C 1; moreover, in this conditional probability distribution, it is not too hard to convince oneself that Ni is uniformly distributed over f1; : : : ; j g. Hence the probability that Oj D ej in this conditional
9.5 Generating a random non-increasing sequence
295
probability distribution is the probability of getting a run of exactly ej copies of the value j in an experiment in which we successively choose numbers between 1 and j at random, and this latter probability is clearly j ej .1 1=j /. 9.5.2 Analysis of the running time Let ` WD len.m/, and let K be the random variable that represents the number of loop iterations performed by the algorithm. With the random variables O2 ; : : : ; Om P defined as above, we can write K D 1 C jmD2 Oj . Moreover, for each j , Oj C 1 has a geometric distribution with associated success probability 1 1=j , and hence EŒOj D
1 1 1=j
1D
1 j
1
:
Thus, EŒK D 1 C
m X
EŒOj D 1 C
j D2
m X1 j D1
1 2C j
Z 1
m
dy D log m C 2; y
where we have estimated the sum by an integral (see §A5). Intuitively, this is roughly as we would expect, since with probability 1=2, each successive ni is at most one half as large as its predecessor, and so after O.`/ steps, we expect to reach 1. Let Z be the total running time of the algorithm. We may bound EŒZ using essentially the same argument that was used in the proof of Theorem 9.3. First, P write Z D i 1 Zi , where Zi is the time spent in the ith loop iteration. Each loop iteration, if executed at all, runs in expected time O.`/. That is, there exists a constant c, such that for each i 1, EŒZi j K i c` and EŒZi j K < i D 0:
Thus, EŒZi D EŒZi j K i PŒK i C EŒZi j K < i PŒK < i c`PŒK i ;
and so EŒZ D
X
EŒZi c`
i 1
X
PŒK i D c`EŒK D O.`2 /:
i 1
In summary, we have shown: the expected running time of Algorithm RS on `-bit inputs is O.`2 /. E XERCISE 9.13. Show that when Algorithm RS runs on input m, the expected number of (not necessarily distinct) primes in the output sequence is log log m.
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Probabilistic algorithms
9.6 Generating a random factored number We now present an efficient algorithm that generates a random factored number. That is, on input m 2, the algorithm generates a number y uniformly distributed over the interval f1; : : : ; mg, but instead of the usual output format for such a number y, the output consists of the prime factorization of y. As far as anyone knows, there are no efficient algorithms for factoring large numbers, despite years of active research in search of such an algorithm. So our algorithm to generate a random factored number will not work by generating a random number and then factoring it. Our algorithm will use Algorithm RS in §9.5 as a subroutine. In addition, as we did in §9.4, we shall assume the existence of an efficient, deterministic primality test IsPrime. In the analysis of the algorithm, we shall make use of Mertens’ theorem, which we proved in Chapter 5 (Theorem 5.13). Algorithm RFN. On input m, where m is an integer 2, do the following: repeat run Algorithm RS on input m, obtaining .n1 ; : : : ; nk / ./ let .p1 ; : : : ; pr / be the subsequence of primes in .n1 ; : : : ; nk / ./ y p1 pr if y m then x R f1; : : : ; mg if x y then output .p1 ; : : : ; pr / and halt forever Notes: ./ For i D 1; : : : ; k 1, the number ni is tested for primality using algorithm IsPrime. The sequence .n1 ; : : : ; nk / may contain duplicates, and if these are prime, they are appear in .p1 ; : : : ; pr / with the same multiplicity. ./ We assume that the product is computed by a simple iterative procedure that halts as soon as the partial product exceeds m. This ensures that the time spent forming the product is always O.len.m/2 /, which simplifies the analysis. We now analyze the running time and output distribution of Algorithm RFN on input m, using the generate-and-test paradigm discussed in §9.3; here, the “generate” part consists of the first two lines of the loop body, which generates the sequence .p1 ; : : : ; pr /, while the “test” part consists of the last four lines of the loop body. Let ` WD len.m/. We assume that each call to IsPrime takes time at most .`/, and for simplicity, we assume .`/ D .`/. Let K1 be the value of k in the first loop iteration, Z1 be the running time of
9.6 Generating a random factored number
297
the first loop iteration, Y1 be the value of y in the first loop iteration, and H1 be the event that the algorithm halts in the first loop iteration. Also, let Z be the total running time of the algorithm, and let Y be the value of y in the last loop iteration (i.e., the number whose factorization is output). We begin with three preliminary calculations. First, let t D 1; : : : ; m be a fixed integer, and let us calculate the probability that Q Y1 D t. Suppose t D pm p ep is the prime factorization of t. Let O2 ; : : : ; Om be random variables as defined in §9.5, so that Oj represents the number of occurrences of j in the output sequence of the first invocation of Algorithm RS. Then Y1 D t if and only if Op D ep for all primes p m, and so by the analysis in §9.5, we have Y g.m/ PŒY1 D t D p ep .1 1=p/ D ; t pm where g.m/ WD
Y
.1
1=p/:
pm
Second, we calculate PŒH1 . Observe that for t D 1; : : : ; m, we have PŒH1 j Y1 D t D t =m;
and so PŒH1 D
m X
PŒH1 j Y1 D t PŒY1 D t D
t D1
m X t g.m/ D g.m/: m t t D1
Third, let t D 1; : : : ; m be a fixed integer, and let us calculate the conditional probability that Y1 D t given H1 . We have PŒY1 D t j H1 D
PŒ.Y1 D t / \ H1
D
PŒH1 j Y1 D t PŒY1 D t
PŒH1 .t =m/.g.m/=t / 1 D D : g.m/ m
PŒH1
We may now easily analyze the output distribution of Algorithm RFN. By Theorem 9.3, for each t D 1; : : : ; m, we have 1 ; m which shows that the output is indeed uniformly distributed over all integers in f1; : : : ; mg, represented in factored form. Finally, we analyze the expected running time of Algorithm RFN. It is easy to PŒY D t D PŒY1 D t j H1 D
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Probabilistic algorithms
see that EŒZ1 D O.EŒK1 .`/ C `2 /, and by the analysis in §9.5, we know that EŒK1 D O.`/, and hence EŒZ1 D O.` .`//. By Theorem 9.3, we have EŒZ D EŒZ1 =PŒH1 D EŒZ1 g.m/
By Mertens’ theorem, g.m/
1
1
:
D O.`/. We conclude that EŒZ D O.`2 .`//:
That is, the expected running time of Algorithm RFN is O.`2 .`//. 9.6.1 Using a probabilistic primality test ./ Analogous to the discussion in §9.4.1, we can analyze the behavior of Algorithm RFN under the assumption that IsPrime is a probabilistic algorithm which may erroneously indicate that a composite number is prime with probability bounded by . Let ` WD len.m/, and as we did in §9.4.1, let N .`/ be a bound on the expected running time of IsPrime for all inputs of bit length at most ` (and again, assume .`/ N D .`/). The random variables K1 ; Z1 ; Y1 ; Z ; Y and the event H1 are defined as above. Let us also define F1 to be the event that the primality test makes a mistake in the first loop iteration, and F to be the event that the output of the algorithm is not a list of primes. Let ı WD PŒF1 . Again, we begin with three preliminary calculations. First, let t D 1; : : : ; m be fixed and let us calculate PŒ.Y1 D t / \ Fx1 . To do this, define the random variable Y10 to be the product of the actual primes among the output of the first invocation of Algorithm RS (because the primality test may err, Y1 may contain additional factors). Evidently, the events .Y1 D t / \ Fx1 and .Y10 D t/\ Fx1 are the same. Moreover, we claim that the events Y10 D t and Fx1 are independent. To see this, recall that the family fOj gjmD2 is mutually independent, and also observe that the event Y10 D t depends only on the random variables Oj , where j is prime, while the event Fx1 depends only on the random variables Oj , where j is composite, along with the execution paths of IsPrime on corresponding inputs. Thus, by a calculation analogous to one we made above, x1 D PŒY10 D t PŒFx1 D PŒ.Y1 D t / \ F
g.m/ .1 t
ı/:
Second, we calculate PŒH1 \ Fx1 . Observe that for t D 1; : : : ; m, we have x1 D t =m; PŒH1 j .Y1 D t / \ F
299
9.6 Generating a random factored number
and so x1 D PŒH1 \ F
m X
x1 PŒH1 \ .Y1 D t / \ F
t D1
D D
m X t D1 m X t D1
x1 PŒ.Y1 D t / \ Fx1 PŒH1 j .Y1 D t / \ F t g.m/ .1 m t
ı/ D g.m/.1
ı/:
Third, let t D 1; : : : ; m be a fixed integer, and let us calculate the conditional probability that .Y1 D t / \ Fx1 given H1 . We have x1 j H1 D PŒ.Y1 D t/ \ F D D
x1 \ H1 PŒ.Y1 D t / \ F PŒH1
x1 PŒ.Y1 D t / \ Fx1 PŒH1 j .Y1 D t / \ F PŒH1 ı/g.m/=t / g.m/.1 ı/ D : PŒH1 mPŒH1
.t =m/..1
We may now easily analyze the output distribution of Algorithm RFN. By Theorem 9.3, for each t D 1; : : : ; m, we have x D PŒ.Y1 D t / \ Fx1 j H1 D PŒ.Y D t/ \ F
g.m/.1 ı/ : mPŒH1
Thus, every integer between 1 and m is equally likely to be output by Algorithm RFN in correct factored form. Let us also calculate an upper bound on the probability PŒF that Algorithm RFN outputs an integer that is not in correct factored form. Making use of Exercise 8.1, we have PŒF1 PŒF1 \ H1 PŒF1 j H1 D : PŒH1 PŒF1 [ H1 Moreover, x1 D ı C g.m/.1 PŒF1 [ H1 D PŒF1 C PŒH1 \ F g.m/ı C g.m/.1
ı/ D g.m/:
By Theorem 9.3, it follows that PŒF D PŒF1 j H1 ı=g.m/:
Now, the reader may verify that ı .EŒK1
1/;
ı/
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Probabilistic algorithms
and by our calculations in §9.5, EŒK1 log m C 2. Thus, ı .log m C 1/; and so by Mertens’ theorem, PŒF D O.`2 /:
We may also analyze the statistical distance between the output distribution of Algorithm RFN and the uniform distribution on f1; : : : ; mg (in factored form). It follows from Theorem 8.31 that D PŒF ı=g.m/ D O.`2 /. Finally, we analyze the expected running time of Algorithm RFN. We have x1 D g.m/.1 PŒH1 PŒH1 \ F
ı/:
We leave it to the reader to verify that EŒZ1 D O.` N .`//, from which it follows by Theorem 9.3 that EŒZ D EŒZ1 =PŒH1 D O.`2 .`/=.1 N
ı//:
If is moderately small, so that .log m C 1/ 1=2, and hence ı 1=2, then EŒZ D O.`2 N .`//:
9.7 Some complexity theory We close this chapter with a few observations about probabilistic algorithms from a more “complexity theoretic” point of view. Suppose f is a function mapping bit strings to bit strings. We may have an algorithm A that approximately computes f in the following sense: there exists a constant , with 0 < 1=2, such that for all inputs x, A.x/ outputs f .x/ with probability at least 1 . The value is a bound on the error probability, which is defined as the probability that A.x/ does not output f .x/. 9.7.1 Reducing the error probability There is a standard “trick” by which one can make the error probability very small; namely, run A on input x some number, say k, times, and take the majority output as the answer. Suppose < 1=2 is a bound on the error probability. Using the Chernoff bound (Theorem 8.24), the error probability for the iterated version of A is bounded by expŒ .1=2
/2 k=2;
(9.8)
and so the error probability decreases exponentially with the number of iterations. This bound is derived as follows. For i D 1; : : : ; k, let Xi be the indicator variable
9.7 Some complexity theory
301
for the event that the i th iteration of A.x/ does not output f .x/. The expected value P x WD 1 k Xi is at most , and if the majority output of the of the sample mean X i D1 k x exceeds its iterated algorithm is wrong (or indeed, if there is no majority), then X expectation by at least 1=2 . The bound (9.8) follows immediately from part (i) of Theorem 8.24. 9.7.2 Strict polynomial time If we have an algorithm A that runs in expected polynomial time, and which approximately computes a function f , then we can easily turn it into a new algorithm A0 that runs in strict polynomial time, and also approximates f , as follows. Suppose that < 1=2 is a bound on the error probability, and P .n/ is a polynomial bound on the expected running time for inputs of size n. Then A0 simply runs A for at most kP .n/ steps, where k is any constant chosen so that C 1=k < 1=2— if A does not halt within this time bound, then A0 simply halts with an arbitrary output. The probability that A0 errs is at most the probability that A errs plus the probability that A runs for more than kP .n/ steps. By Markov’s inequality (Theorem 8.22), the latter probability is at most 1=k, and hence A0 approximates f as well, but with an error probability bounded by C 1=k. 9.7.3 Language recognition An important special case of approximately computing a function is when the output of the function f is either 0 or 1 (or equivalently, false or true). In this case, f may be viewed as the characteristic function of the language L WD fx W f .x/ D 1g. (It is the tradition of computational complexity theory to call sets of bit strings “languages.”) There are several “flavors” of probabilistic algorithms for approximately computing the characteristic function f of a language L that are traditionally considered — for the purposes of these definitions, we may restrict ourselves to algorithms that output either 0 or 1: We call a probabilistic, expected polynomial-time algorithm an Atlantic City algorithm for recognizing L if it approximately computes f with error probability bounded by a constant < 1=2. We call a probabilistic, expected polynomial-time algorithm A a Monte Carlo algorithm for recognizing L if for some constant ı > 0, we have: – PŒA.x/ outputs 1 ı for all x 2 L; – PŒA.x/ outputs 1 D 0 for all x … L. We call a probabilistic, expected polynomial-time algorithm a Las Vegas algorithm for recognizing L if it computes f correctly on all inputs x.
302
Probabilistic algorithms
One also says an Atlantic City algorithm has two-sided error, a Monte Carlo algorithm has one-sided error, and a Las Vegas algorithm has zero-sided error. E XERCISE 9.14. Show that every language recognized by a Las Vegas algorithm is also recognized by a Monte Carlo algorithm, and that every language recognized by a Monte Carlo algorithm is also recognized by an Atlantic City algorithm. E XERCISE 9.15. Show that if L is recognized by an Atlantic City algorithm that runs in expected polynomial time, then it is recognized by an Atlantic City algorithm that runs in strict polynomial time, and whose error probability is at most 2 n on inputs of size n. E XERCISE 9.16. Show that if L is recognized by a Monte Carlo algorithm that runs in expected polynomial time, then it is recognized by a Monte Carlo algorithm that runs in strict polynomial time, and whose error probability is at most 2 n on inputs of size n. E XERCISE 9.17. Show that a language is recognized by a Las Vegas algorithm if and only if the language and its complement are recognized by Monte Carlo algorithms. E XERCISE 9.18. Show that if L is recognized by a Las Vegas algorithm that runs in strict polynomial time, then L may be recognized in deterministic polynomial time. E XERCISE 9.19. Suppose that for a given language L, there exists a probabilistic algorithm A that runs in expected polynomial time, and always outputs either 0 or 1. Further suppose that for some constants ˛ and c, where ˛ is a rational number with 0 ˛ < 1, and c is a positive integer, and for all sufficiently large n, and all inputs x of size n, we have if x … L, then PŒA.x/ outputs 1 ˛, and if x 2 L, then PŒA.x/ outputs 1 ˛ C 1=nc . (a) Show that there exists an Atlantic City algorithm for L. (b) Show that if ˛ D 0, then there exists a Monte Carlo algorithm for L. 9.8 Notes Our approach in §9.1 to defining the probability distribution associated with the execution of a probabilistic algorithm is not the only possible approach. Another
9.8 Notes
303
approach is to define the output distribution and expected running time of an algorithm on a given input directly, using the identities in Exercise 9.2, and avoid the construction of an underlying probability distribution altogether. However, without such a probability distribution, we would have very few tools at our disposal to analyze the output distribution and running time of particular algorithms. Yet another approach is to define a distribution that models an infinite random bit string. This can be done, but requires more advanced notions from probability theory than those that have been covered in this text. The algorithm presented in §9.6 for generating a random factored number is due to Kalai [51], although the analysis presented here is a bit different, and our analysis using a probabilistic primality test is new. Kalai’s algorithm is significantly simpler, though less efficient, than an earlier algorithm due to Bach [9], which uses an expected number of O.`/ primality tests, as opposed to the O.`2 / primality tests used by Kalai’s algorithm. See Luby [61] for an exposition of the theory of pseudo-random bit generation.
10 Probabilistic primality testing
In this chapter, we discuss some simple and efficient probabilistic algorithms for testing whether a given integer is prime. 10.1 Trial division Suppose we are given an integer n > 1, and we want to determine whether n is prime or composite. The simplest algorithm to describe and to program is trial division. We simply divide n by 2, 3, and so on, testing if any of these numbers p evenly divide n. Of course, we don’t need to go any further than n, since if n p has any non-trivial factors, it must have one that is no greater than n (see Exercise 1.2). Not only does this algorithm determine whether n is prime or composite, it also produces a non-trivial factor of n in case n is composite. Of course, the drawback of this algorithm is that it is terribly inefficient: it rep quires ‚. n/ arithmetic operations, which is exponential in the bit length of n. Thus, for practical purposes, this algorithm is limited to quite small n. Suppose, for example, that n has 100 decimal digits, and that a computer can perform 1 billion divisions per second (this is much faster than any computer existing today). p Then it would take on the order of 1033 years to perform n divisions. In this chapter, we discuss a much faster primality test that allows 100-decimaldigit numbers to be tested for primality in less than a second. Unlike the above test, however, this test does not find a factor of n when n is composite. Moreover, the algorithm is probabilistic, and may in fact make a mistake. However, the probability that it makes a mistake can be made so small as to be irrelevant for all practical purposes. Indeed, we can easily make the probability of error as small as 2 100 — should one really care about an event that happens with such a miniscule probability?
304
10.2 The Miller–Rabin test
305
10.2 The Miller–Rabin test We describe in this section a fast (polynomial time) test for primality, known as the Miller–Rabin test. The algorithm, however, is probabilistic, and may (with small probability) make a mistake. We assume for the remainder of this section that the number n we are testing for primality is an odd integer greater than 1. We recall some basic algebraic facts that will play a critical role in this section (see §7.5). Suppose n D p1e1 prer is the prime factorization of n (since n is odd, each pi is odd). The Chinese remainder theorem gives us a ring isomorphism W
Zn ! Zpe1 Zprer 1
Œan 7! .Œape1 ; : : : ; Œaprer /; 1
and restricting to
Zn
yields a group isomorphism Zn Š Ze1 Zper : p1
r
Moreover, Theorem 7.28 says that each Zei is a cyclic group, whose order, of .piei /
pi
piei 1 .pi
course, is D 1/. Several probabilistic primality tests, including the Miller–Rabin test, have the following general structure. Define ZC n to be the set of non-zero elements of Zn ; thus, jZC j D n 1, and if n is prime, ZC n n D Zn . Suppose also that we define a set Ln ZC n such that: there is an efficient algorithm that on input n and ˛ 2 ZC n , determines if ˛ 2 Ln ; if n is prime, then Ln D Zn ; if n is composite, jLn j c.n
1/ for some constant c < 1.
To test n for primality, we set a “repetition parameter” k, and choose random elements ˛1 ; : : : ; ˛k 2 ZC n . If ˛i 2 Ln for all i D 1; : : : ; k, then we output true; otherwise, we output false. It is easy to see that if n is prime, this algorithm always outputs true, and if n is composite this algorithm outputs true with probability at most c k . If c D 1=2 and k is chosen large enough, say k D 100, then the probability that the output is wrong is so small that for all practical purposes, it is “just as good as zero.” We now make a first attempt at defining a suitable set Ln . Let us define n Ln WD f˛ 2 ZC n W˛
Note that Ln Zn , since if ˛ n
1
1
D 1g:
D 1, then ˛ has a multiplicative inverse,
306
Probabilistic primality testing
namely, ˛ n 2 . Using a repeated-squaring algorithm, we can test if ˛ 2 Ln in time O.len.n/3 /. Theorem 10.1. If n is prime, then Ln D Zn . If n is composite and Ln ¨ Zn , then jLn j .n 1/=2. Proof. Note that Ln is the kernel of the .n 1/-power map on Zn , and hence is a subgroup of Zn . If n is prime, then we know that Zn is a group of order n 1. Since the order of a group element divides the order of the group, we have ˛ n 1 D 1 for all ˛ 2 Zn . That is, Ln D Zn . Suppose that n is composite and Ln ¨ Zn . Since the order of a subgroup divides the order of the group, we have jZn j D tjLn j for some integer t > 1. From this, we conclude that 1 1 n 1 jLn j D jZn j jZn j : t 2 2 Unfortunately, there are odd composite numbers n such that Ln D Zn . Such numbers are called Carmichael numbers. The smallest Carmichael number is 561 D 3 11 17: Carmichael numbers are extremely rare, but it is known that there are infinitely many of them, so we cannot ignore them. The following theorem puts some constraints on Carmichael numbers. Theorem 10.2. Every Carmichael number n is of the form n D p1 pr , where the pi ’s are distinct primes, r 3, and .pi 1/ j .n 1/ for i D 1; : : : ; r. Proof. Let n D p1e1 prer be a Carmichael number. By the Chinese remainder theorem, we have an isomorphism of Zn with the group Ze1 Zper ; p1
r
and we know that each group Zei is cyclic of order piei pi
n
1
.pi 1/. Thus, the power
1 kills the group Zn if and only if it kills all the groups Zei , which happens pi
piei 1 .pi
if and only if 1/ j .n 1/. Now, on the one hand, n 0 .mod pi /. On the other hand, if ei > 1, we would have n 1 .mod pi /, which is clearly impossible. Thus, we must have ei D 1. It remains to show that r 3. Suppose r D 2, so that n D p1 p2 . We have n
1 D p1 p2
1 D .p1
1/p2 C .p2
1/:
Since .p1 1/ j .n 1/, we must have .p1 1/ j .p2 1/. By a symmetric argument, .p2 1/ j .p1 1/. Hence, p1 D p2 , a contradiction.
307
10.2 The Miller–Rabin test
To obtain a good primality test, we need to define a different set L0n , which we do as follows. Let n 1 D t 2h , where t is odd (and h 1 since n is assumed odd), and define h
t 2 D 1 and L0n WD f˛ 2 ZC n W ˛ j j C1 D 1 H) ˛ t 2 D ˙1 for j D 0; : : : ; h ˛t 2
1g:
The Miller–Rabin test uses this set L0n , in place of the set Ln defined above. It is clear from the definition that L0n Ln . 0 Testing whether a given ˛ 2 ZC n belongs to Ln can be done using the following procedure: ˇ ˛t if ˇ D 1 then return true for j 0 to h 1 do if ˇ D 1 then return true if ˇ D C1 then return false ˇ ˇ2 return false It is clear that using a repeated-squaring algorithm, this procedure runs in time O.len.n/3 /. We leave it to the reader to verify that this procedure correctly determines membership in L0n . Theorem 10.3. If n is prime, then L0n D Zn . If n is composite, then jL0n j .n 1/=4. Proof. Let n
1 D t 2h , where t is odd.
Case 1: n is prime. Let ˛ 2 Zn . Since Zn is a group of order n 1, and the order h of a group element divides the order of the group, we know that ˛ t 2 D ˛ n 1 D 1. j C1 Now consider any index j D 0; : : : ; h 1 such that ˛ t 2 D 1, and consider j j C1 the value ˇ WD ˛ t 2 . Then since ˇ 2 D ˛ t 2 D 1, the only possible choices for ˇ are ˙1 — this is because Zn is cyclic of even order and so there are exactly two elements of Zn whose multiplicative order divides 2, namely ˙1. So we have shown that ˛ 2 L0n . Case 2: n D p e , where p is prime and e > 1. Certainly, L0n is contained in the kernel K of the .n 1/-power map on Zn . By Theorem 6.32, jKj D gcd. .n/; n 1/. Since n D p e , we have .n/ D p e 1 .p 1/, and so jL0n j jKj D gcd.p e
1
.p
1/; p e
1/ D p
1D
pe
pe 1 n 1 : 1 C C 1 4
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Probabilistic primality testing
Case 3: n D p1e1 prer is the prime factorization of n, and r > 1. Let W Zn ! Zpe1 Zprer 1
be the ring isomorphism provided by the Chinese remainder theorem. Also, let .piei / D ti 2hi , with ti odd, for i D 1; : : : ; r, and let g WD minfh; h1 ; : : : ; hr g. Note that g 1, and that each Zei is a cyclic group of order ti 2hi . pi
g
We first claim that for every ˛ 2 L0n , we have ˛ t 2 D 1. To prove this, first g note that if g D h, then by definition, ˛ t 2 D 1, so suppose that g < h. By g way of contradiction, suppose that ˛ t 2 ¤ 1, and let j be the smallest index in j C1 the range g; : : : ; h 1 such that ˛ t 2 D 1. By the definition of L0n , we must j have ˛ t 2 D 1. Since g < h, we must have g D hi for some particular index j i D 1; : : : ; r. Writing .˛/ D .˛1 ; : : : ; ˛r /, we have ˛it 2 D 1. This implies that the multiplicative order of ˛it is equal to 2j C1 (see Theorem 6.37). However, since j g D hi , this contradicts the fact that the order of a group element (in this case, ˛it ) must divide the order of the group (in this case, Zei ). pi
For j D 0; : : : ; h, let us define j to be the .t 2j /-power map on Zn . From the claim in the previous paragraph, and the definition of L0n , it follows that each g 1 ˛ 2 L0n satisfies ˛ t 2 D ˙1. In other words, L0n g 1 1 .f˙1g/, and hence jL0n j 2jKer g
1 j:
(10.1)
From the group isomorphism Zn Š Ze1 Zper , and Theorem 6.32, we have p1
jKer j j D
r Y
r
gcd.ti 2hi ; t 2j /
(10.2)
i D1
for each j D 0; : : : ; h. Since g h, and g hi for i D 1; : : : ; r, it follows immediately from (10.2) that 2r jKer g
1j
D jKer g j jKer h j:
(10.3)
Combining (10.3) with (10.1), we obtain jL0n j 2
rC1
jKer h j:
(10.4)
If r 3, then (10.4) directly implies that jL0n j jZn j=4 .n 1/=4, and we are done. So suppose that r D 2. In this case, Theorem 10.2 implies that n is not a Carmichael number, which implies that jKer h j jZn j=2, and so again, (10.4) implies jL0n j jZn j=4 .n 1/=4.
10.3 Generating random primes using the Miller–Rabin test
309
E XERCISE 10.1. Show that an integer n > 1 is prime if and only if there exists an element in Zn of multiplicative order n 1. E XERCISE 10.2. Show that Carmichael numbers satisfy Fermat’s little theorem; that is, if n is a Carmichael number, then ˛ n D ˛ for all ˛ 2 Zn . E XERCISE 10.3. Let p be a prime. Show that n WD 2p C 1 is a prime if and only if 2n 1 1 .mod n/. E XERCISE 10.4. Here is another primality test that takes as input an odd integer n > 1, and a positive integer parameter k. The algorithm chooses ˛1 ; : : : ; ˛k 2 ZC n at random, and computes .n ˇi WD ˛i
1/=2
.i D 1; : : : ; k/:
If .ˇ1 ; : : : ; ˇk / is of the form .˙1; ˙1; : : : ; ˙1/; but is not equal to .1; 1; : : : ; 1/, the algorithm outputs true; otherwise, the algorithm outputs false. Show that if n is prime, then the algorithm outputs false with probability at most 2 k , and if n is composite, the algorithm outputs true with probability at most 2 k . In the terminology of §9.7, the algorithm in the above exercise is an example of an “Atlantic City” algorithm for the language of prime numbers (or equivalently, the language of composite numbers), while the Miller–Rabin test is an example of a “Monte Carlo” algorithm for the language of composite numbers. 10.3 Generating random primes using the Miller–Rabin test The Miller–Rabin test is the most practical algorithm known for testing primality, and because of this, it is widely used in many applications, especially cryptographic applications where one needs to generate large, random primes (as we saw in §4.7). In this section, we discuss how one uses the Miller–Rabin test in several practically relevant scenarios where one must generate large primes. 10.3.1 Generating a random prime between 2 and m Suppose we are given an integer m 2, and want to generate a random prime between 2 and m. We can do this by simply picking numbers at random until one of them passes a primality test. We discussed this problem in some detail in §9.4, where we assumed that we had a primality test IsPrime. The reader should review §9.4, and §9.4.1 in particular. In this section, we discuss aspects of this problem that are specific to the situation where the Miller–Rabin test is used to implement IsPrime. To be more precise, let us define the following algorithm:
310
Probabilistic primality testing
Algorithm MR. On input n; k, where n and k are integers with n > 1 and k 1, do the following: if n D 2 then return true if n is even then return false repeat k times ˛ R ZC n if ˛ … L0n return false return true So we shall implement IsPrime./ as MR.; k/, where k is an auxiliary parameter. By Theorem 10.3, if n is prime, the output of MR.n; k/ is always true, while if n is composite, the output is true with probability at most 4 k . Thus, this implementation of IsPrime satisfies the assumptions in §9.4.1, with D 4 k . Let .m; k/ be the probability that the output of Algorithm RP in §9.4 — using this implementation of IsPrime— is composite. Then as we discussed in §9.4.1,
.m; k/ 4
k
m 1 D O.4 .m/
k
`/;
(10.5)
where ` WD len.m/. Furthermore, if the output of Algorithm RP is prime, then every prime is equally likely; that is, the conditional distribution of the output, given that the output is prime, is essentially the uniform distribution on the set of primes up to m. Let us now consider the expected running time of Algorithm RP. As discussed in §9.4.1, the expected number of iterations of the main loop in Algorithm RP is O.`/. Clearly, the expected running time of a single loop iteration is O.k`3 /, since MR.n; k/ executes at most k iterations of the Miller–Rabin test, and each such test takes time O.`3 /. This leads to a bound on the expected total running time of Algorithm RP of O.k`4 /. However, this estimate is overly pessimistic, because when n is composite, we expect to perform very few Miller–Rabin tests — only when n is prime do we actually perform all k of them. To make a rigorous argument, let us define random variables measuring various quantities during the first iteration of the main loop in Algorithm RP: N1 , the value of n; K1 , the number of Miller–Rabin tests actually performed; Z1 , the running time. Of course, N1 is uniformly distributed over f2; : : : ; mg. Let C1 be the event that N1 is composite. Consider the conditional distribution of K1 given C1 . This is not exactly a geometric distribution, since K1 never takes on values greater than k; nevertheless, using Theorem 8.17, we can easily calculate X X EŒK1 j C1 D PŒK1 i j C1 .1=4/i 1 D 4=3: i 1
i 1
10.3 Generating random primes using the Miller–Rabin test
311
Using (8.24), it follows that EŒK1 D EŒK1 j C1 PŒC1 C EŒK1 j Cx1 PŒCx1
4=3 C k.m/=.m
1/:
Thus, EŒK1 4=3 C O.k=`/, and hence EŒZ1 D O.`3 EŒK1 / D O.`3 C k`2 /. Therefore, if Z is the total running time of Algorithm RP, then EŒZ D O.`EŒZ1 /, and so EŒZ D O.`4 C k`3 /:
(10.6)
Note that the above estimate (10.5) for .m; k/ is actually quite pessimistic. This is because the error probability 4 k is a worst-case estimate; in fact, for “most” composite integers n, the probability that MR.n; k/ outputs true is much smaller than this. In fact, .m; 1/ is very small for large m. For example, the following is known: Theorem 10.4. We have
.m; 1/ expŒ .1 C o.1// log.m/ log.log.log.m///= log.log.m//: Proof. Literature—see §10.5. The bound in the above theorem goes to zero quite quickly — faster than .log m/ c for every positive constant c. While the above theorem is asymptotically very good, in practice, one needs explicit bounds. For example, the following lower bounds for log2 . .2` ; 1// are known: `
200 3
300 19
400 37
500 55
600 74
Given an upper bound on .m; 1/, we can bound .m; k/ for k 2 using the following inequality:
.m; k/
.m; 1/ 4 1 .m; 1/
kC1
:
(10.7)
To prove (10.7), it is not hard to see that on input m, the output distribution of Algorithm RP is the same as that of the following algorithm: repeat repeat n0 R f2; : : : ; mg until MR.n0 ; 1/ n n0 until MR.n; k 1/ output n
312
Probabilistic primality testing
Let N1 be the random variable representing the value of n in the first iteration of the main loop in this algorithm, let C1 be the event that N1 is composite, and let H1 be the event that this algorithm halts at the end of the first iteration of the main loop. Using Theorem 9.3, we see that
.m; k/ D PŒC1 j H1 D
kC1 .m; 1/
4 1
.m; 1/
PŒC1 \ H1 PŒH1
PŒC1 \ H1 PŒCx1
D
PŒH1 j C1 PŒC1 PŒCx1
;
which proves (10.7). Given that .m; 1/ is so small, for large m, Algorithm RP actually exhibits the following behavior in practice: it generates a random value n 2 f2; : : : ; mg; if n is odd and composite, then the very first iteration of the Miller–Rabin test will detect this with overwhelming probability, and no more iterations of the test are performed on this n; otherwise, if n is prime, the algorithm will perform k 1 more iterations of the Miller–Rabin test, “just to make sure.” E XERCISE 10.5. Consider the problem of generating a random Sophie Germain prime between 2 and m (see §5.5.5). One algorithm to do this is as follows: repeat n R f2; : : : ; mg if MR.n; k/ then if MR.2n C 1; k/ then output n and halt forever Assuming Conjecture 5.24, show that this algorithm runs in expected time O.`5 C k`4 /, and outputs a number that is not a Sophie Germain prime with probability O.4 k `2 /. As usual, ` WD len.m/. E XERCISE 10.6. Improve the algorithm in the previous exercise, so that under the same assumptions, it runs in expected time O.`5 C k`3 /, and outputs a number that is not a Sophie Germain prime with probability O.4 k `2 /, or even better, show that this probability is at most .m; k/ .m/=.m/ D O. .m; k/`/, where .m/ is defined as in §5.5.5. E XERCISE 10.7. Suppose in Algorithm RFN in §9.6 we implement algorithm IsPrime./ as MR.; k/, where k is a parameter satisfying 4 k .log m C 1/ 1=2, and m is the input to RFN. Show that the expected running time of Algorithm RFN in this case is O.`5 C k`4 len.`//. Hint: use Exercise 9.13.
10.3 Generating random primes using the Miller–Rabin test
313
10.3.2 Trial division up to a small bound In generating a random prime, most candidates will in fact be composite, and so it makes sense to cast these out as quickly as possible. Significant efficiency gains can be achieved by testing if a given candidate n is divisible by any small primes up to a given bound s, before we subject n to a Miller–Rabin test. This strategy makes sense, since for a small, “single precision” prime p, we can test if p j n essentially in time O.len.n//, while a single iteration of the Miller–Rabin test takes time O.len.n/3 /. To be more precise, let us define the following algorithm: Algorithm MRS. On input n; k; s, where n; k; s 2 Z, and n > 1, k 1, and s > 1, do the following: for each prime p s do if p j n then if p D n then return true else return false repeat k times ˛ R ZC n if ˛ … L0n return false return true In an implementation of the above algorithm, one would most likely use the sieve of Eratosthenes (see §5.4) to generate the small primes. Note that MRS.n; k; 2/ is equivalent to MR.n; k/. Also, it is clear that the probability that MRS.n; k; s/ makes a mistake is no more than the probability that MR.n; k/ makes a mistake. Therefore, using MRS in place of MR will not increase the probability that the output of Algorithm RP is a composite — indeed, it is likely that this probability decreases significantly. Let us now analyze the impact on the running time Algorithm RP. To do this, we need to estimate the probability .m; s/ that a randomly chosen integer between 2 and m is not divisible by any primes up to s. If m is sufficiently large with respect to s, the following heuristic argument can be made rigorous, as we will discuss below. The probability that a random integer is divisible by a prime p is about 1=p, so the probability that it is not divisible by p is about 1 1=p. Assuming that these events are essentially independent for different values of p (this is the heuristic part), we estimate Y .m; s/ .1 1=p/: (10.8) ps
Assuming for the time being that the approximation in (10.8) is sufficiently accu-
314
Probabilistic primality testing
rate, then using Mertens’ theorem (Theorem 5.13), we may deduce that .m; s/ D O.1= log s/:
(10.9)
Later, when we make this argument more rigorous, we shall see that (10.9) holds provided s is not too large relative to m, and in particular, if s D O..log m/c / for some constant c. The estimate (10.9) gives us a bound on the probability that a random integer passes the trial division phase, and so must be subjected to Miller–Rabin; however, performing the trial division takes some time, so we also need to estimate the expected number .m; s/ of trial divisions performed on a random integer between 2 and m. Of course, in the worst case, we divide by all primes up to s, and so .m; s/ .s/ D O.s= log s/, but we can get a better bound, as follows. Let p1 ; p2 ; : : : ; pr be the primes up to s, and for i D 1; : : : ; r, let qi be the probability that we perform at least i trial divisions. By Theorem 8.17, we have .m; s/ D
r X
qi :
i D1
Moreover, q1 D 1, and qi D .m; pi it follows that .m; s/ D 1 C
r X
1/
for i D 2; : : : ; r. From this, and (10.9),
.m; pi
i D2
1/ D O
X
1= log p :
ps
As a simple consequence of Chebyshev’s theorem (in particular, see Exercise 5.3), we obtain .m; s/ D O.s=.log s/2 /:
(10.10)
We now derive a bound on the running time of Algorithm RP, assuming IsPrime./ is implemented using MRS.; k; s/. Let ` WD len.m/. Our argument follows the same lines as was used to derive the estimate (10.6). Let us define random variables measuring various quantities during the first iteration of the main loop in Algorithm RP: N1 , the value of n; K1 , the number of Miller–Rabin tests actually performed; Z1 , the running time. Let C1 be the event that N1 is composite, and let D1 be the event that N1 passes the trial division check. Then we have x1 PŒC1 \ D x1 EŒK1 D EŒK1 j C1 \ D1 PŒC1 \ D1 C EŒK1 j C1 \ D C EŒK1 j Cx1 PŒCx1 x1 C k PŒCx1 4=3 PŒC1 \ D1 C 0 PŒC1 \ D 4=3 PŒD1 C k PŒCx1 :
10.3 Generating random primes using the Miller–Rabin test
315
By (10.9) and Chebyshev’s theorem, it follows that EŒK1 D O.1= len.s/ C k=`/:
(10.11)
Let us write Z1 D Z10 C Z100 , where Z10 is the amount of time spent performing the Miller–Rabin test, and Z100 is the amount of time spent performing trial division. By (10.11), we have EŒZ10 D O.`3 = len.s/ C k`2 /. Further, assuming that each individual trial division step takes time O.`/, then by (10.10) we have EŒZ100 D O.`s= len.s/2 /. Hence, EŒZ1 D O.`3 = len.s/ C k`2 C `s= len.s/2 /:
It follows that if Z is the total running time of Algorithm RP, then EŒZ D O.`4 = len.s/ C k`3 C `2 s= len.s/2 /:
Clearly, we want to choose the parameter s so that the time spent performing trial division is dominated by the time spent performing the Miller–Rabin test. To this end, let us assume that ` s `2 . Then we have EŒZ D O.`4 = len.`/ C k`3 /:
(10.12)
This estimate does not take into account the time to generate the small primes using the sieve of Eratosthenes. These values might be pre-computed, in which case this time is zero, but even if we compute them on the fly, this takes time O.s len.len.s///, which is dominated by the running time of the rest of the algorithm for the values of s under consideration. Thus, by sieving up to a bound s, where ` s `2 , then compared to (10.6), we effectively reduce the running time by a factor proportional to len.`/, which is a very real and noticeable improvement in practice. As we already mentioned, the above analysis is heuristic, but the results are correct. We shall now discuss how this analysis can be made rigorous; however, we should remark that any such rigorous analysis is mainly of theoretical interest only — in any practical implementation, the optimal choice of the parameter s is best determined by experiment, with the analysis being used only as a rough guide. Now, to make the analysis rigorous, we need prove that the estimate (10.8) is sufficiently accurate. Proving such estimates takes us into the realm of “sieve theory.” The larger m is with respect to s, the easier it is to prove such estimates. We shall prove only the simplest and most naive such estimate, but it is still good enough for our purposes. Before stating any results, let us restate the problem slightly. For real y 0, let us call a positive integer “y-rough” if it is not divisible by any prime p up to y. For real x 0, let us define R.x; y/ to be the number of y-rough positive integers up to
316
Probabilistic primality testing
x. Thus, since .m; s/ is the probability that a random integer between 2 and m is s-rough, and 1 is by definition s-rough, we have .m; s/ D .R.m; s/ 1/=.m 1/. Theorem 10.5. For all real x 0 and y 0, we have ˇ ˇ Y ˇ ˇ ˇR.x; y/ x .1 1=p/ˇˇ 2.y/ : ˇ py
Proof. To simplify the notation, we shall use the Möbius function (see §2.9). Also, for a real number u, let us write u D buc C fug, where 0 fug < 1. Let P be the product of the primes up to the bound y. Now, there are bxc positive integers up to x, and of these, for each prime p dividing P , precisely bx=pc are divisible by p, for each pair p; p 0 of distinct primes dividing P , precisely bx =pp 0 c are divisible by pp 0 , and so on. By inclusion/exclusion (see Theorem 8.1), we have X X X R.x; y/ D .d /bx=d c D .d /.x=d / .d /fx=d g: d jP
d jP
d jP
Moreover, X
.d /.x=d / D x
d jP
X
.d /=d D x
Y
.1
1=p/;
py
d jP
and ˇX ˇ X ˇ ˇ ˇ .d /fx=d gˇˇ 1 D 2.y/ : ˇ d jP
d jP
That proves the theorem. This theorem says something non-trivial only when y is quite small. Nevertheless, using Chebyshev’s theorem on the density of primes, along with Mertens’ theorem, it is not hard to see that this theorem implies that (10.9) holds when s D O..log m/c / for some constant c (see Exercise 10.8), which implies the estimate (10.12) above, when ` s `2 . E XERCISE 10.8. Suppose that s is a function of m such that s D O..log m/c / for some positive constant c. Show that .m; s/ D O.1= log s/. E XERCISE 10.9. Let f be a polynomial with integer coefficients. For real x 0 and y 0, define Rf .x; y/ to be the number of positive integers t up to x such that f .t/ is y-rough. For each positive integer m, define !f .m/ to be the number of integers t 2 f0; : : : ; m 1g such that f .t / 0 .mod m/. Show that ˇ ˇ Y Y ˇ ˇ ˇRf .x; y/ x .1 !f .p/=p/ˇˇ .1 C !f .p//: ˇ py
py
10.3 Generating random primes using the Miller–Rabin test
317
E XERCISE 10.10. Consider again the problem of generating a random Sophie Germain prime, as discussed in Exercises 10.5 and 10.6. A useful idea is to first test if either n or 2n C 1 are divisible by any small primes up to some bound s, before performing any more expensive tests. Using this idea, design and analyze an algorithm that improves the running time of the algorithm in Exercise 10.6 to O.`5 = len.`/2 Ck`3 /— under the same assumptions, and achieving the same error probability bound as in that exercise. Hint: first show that the previous exercise implies that the number of positive integers t up to x such that both t and 2t C 1 are y-rough is at most 1 Y x .1 2=p/ C 3.y/ : 2 2
E XERCISE 10.11. Design an algorithm that takes as input a prime q and a bound m, and outputs a random prime p between 2 and m such that p 1 .mod q/. Clearly, we need to assume that m is sufficiently large with respect to q. Analyze your algorithm assuming Conjecture 5.22. State how large m must be with respect to q, and under these assumptions, show that your algorithm runs in time O.`4 = len.`/ C k`3 /, and that its output is incorrect with probability O.4 k `/. As usual, ` WD len.m/. 10.3.3 Generating a random `-bit prime In some applications, we want to generate a random prime of fixed size — a random 1024-bit prime, for example. More generally, let us consider the following problem: given an integer ` 2, generate a random `-bit prime, that is, a prime in the interval Œ2` 1 ; 2` /. Bertrand’s postulate (Theorem 5.8) implies that there exists a constant c > 0 such that .2` / .2` 1 / c2` 1 =` for all ` 2. Now let us modify Algorithm RP so that it takes as input an integer ` 2, and repeatedly generates a random n in the interval f2` 1 ; : : : ; 2` 1g until IsPrime.n/ returns true. Let us call this variant Algorithm RP0 . Further, let us implement IsPrime./ as MR.; k/, for some auxiliary parameter k, and define 0 .`; k/ to be the probability that the output of Algorithm RP0 — with this implementation of IsPrime—is composite. Then using exactly the same reasoning as in §10.3.1, we have
0 .`; k/ 4
2`
k
.2` /
1
.2` 1 /
D O.4
k
`/I
moreover, if the output of Algorithm RP0 is prime, then every `-bit prime is equally
318
Probabilistic primality testing
likely, and the expected running time is O.`4 C k`3 /. By doing some trial division as in §10.3.2, this can be reduced to O.`4 = len.`/ C k`3 /. The function 0 .`; k/ has been studied a good deal; for example, the following explicit bound is known: Theorem 10.6. For all ` 2, we have
0 .`; 1/ `2 42
p `
:
Proof. Literature—see §10.5. Upper bounds for 0 .`; k/ for specific values of ` and k have been computed. The following table lists some known lower bounds for log2 . 0 .`; k// for various values of ` and k: kn` 1 2 3 4 5
200 11 25 34 41 47
300 19 33 44 53 60
400 37 46 55 63 72
500 56 63 70 78 85
600 75 82 88 95 102
Using exactly the same reasoning as the derivation of (10.7), one sees that
0 .`; k/
0 .`; 1/ 4 1 0 .`; 1/
kC1
:
10.4 Factoring and computing Euler’s phi function In this section, we use some of the ideas developed to analyze the Miller–Rabin test to prove that the problem of factoring n and the problem of computing .n/ are equivalent. By equivalent, we mean that given an efficient algorithm to solve one problem, we can efficiently solve the other, and vice versa. Clearly, one direction is easy: if we can factor n into primes, so n D p1e1 prer ;
(10.13)
then we can simply compute .n/ using the formula .n/ D p1e1
1
.p1
1/ prer
1
.pr
1/:
For the other direction, first consider the special case where n D pq, for distinct primes p and q. Suppose we are given n and .n/, so that we have two equations in the unknowns p and q: n D pq and .n/ D .p
1/.q
1/:
10.4 Factoring and computing Euler’s phi function
319
Substituting n=p for q in the second equation, and simplifying, we obtain p 2 C . .n/
n
1/p C n D 0;
which can be solved using the quadratic formula. For the general case, it is just as easy to prove a stronger result: given any nonzero multiple of the exponent of Zn , we can efficiently factor n. In particular, this will show that we can efficiently factor Carmichael numbers. Before stating the algorithm in its full generality, we can convey the main idea by considering the special case where n D pq, where p and q are distinct primes, with p q 3 .mod 4/. Suppose we are given such an n, along with a non-zero multiple f of the exponent of Zn . Now, Zn Š Zp Zq , and since Zp is a cyclic group of order p 1 and Zq is a cyclic group of order q 1, this means that f is a non-zero common multiple of p 1 and q 1. Let f D t 2h , where t is odd, and consider the following probabilistic algorithm: ˛ R ZC n d gcd.rep.˛/; n/ if d ¤ 1 then output d and halt ˇ ˛t 0 d gcd.rep.ˇ/ C 1; n/ 0 if d … f1; ng then output d 0 and halt output “failure” Recall that rep.˛/ denotes the canonical representative of ˛, that is, the unique integer a such that Œan D ˛ and 0 a < n. We shall prove that this algorithm outputs a non-trivial divisor of n with probability at least 1=2. Let be the t -power map on Zn , and let G WD 1 .f˙1g/. We shall show that G ¨ Zn , and if the algorithm chooses ˛ … G, then it splits n. Since G is a subgroup of Zn , it follows that jGj=jZC n j jGj=jZn j 1=2, and this implies the algorithm succeeds with probability at least 1=2. Let W Zn ! Zp Zq be the ring isomorphism from the Chinese remainder theorem. The assumption that p 3 .mod 4/ means that .p 1/=2 is an odd integer, and since f is a multiple of p 1, it follows that gcd.t; p 1/ D .p 1/=2, and hence the image of Zp under the t -power map is the subgroup of Zp of order 2, which is f˙1g. Likewise, the image of Zq under the t-power map is f˙1g. Thus, .Im / D ..Zn /t / D ..Zn //t D .Zp /t .Zq /t D f˙1g f˙1g; and so Im consists of the four elements: 1D
1
.1; 1/;
1D
1
. 1; 1/;
1
. 1; 1/;
1
.1; 1/:
320
Probabilistic primality testing
By the observations in the previous paragraph, not all elements of Zn map to ˙1 under , which means that G ¨ Zn . Suppose that the algorithm chooses ˛ 2 ZC n n G. We want to show that n gets split. If ˛ … Zn , then gcd.rep.˛/; n/ is a non-trivial divisor of n, and the algorithm splits n. So let us assume that ˛ 2 Zn n G. Consider the value ˇ D ˛ t D .˛/ computed by the algorithm. Since ˛ … G, we have ˇ ¤ ˙1, and by the observations in the previous paragraph, we have .ˇ/ D . 1; 1/ or .ˇ/ D .1; 1/. In the first case, .ˇ C 1/ D .0; 2/, and so gcd.rep.ˇ/ C 1; n/ D p, while in the second case, .ˇ C 1/ D .2; 0/, and so gcd.rep.ˇ/ C 1; n/ D q. In either case, the algorithm splits n. We now consider the general case, where n is an arbitrary positive integer. Let .n/ denote the exponent of Zn . If the prime factorization of n is as in (10.13), then by the Chinese remainder theorem, we have .n/ D lcm..p1e1 /; : : : ; .prer //: Moreover, for every prime power p e , by Theorem 7.28, we have e 1 p .p 1/ if p ¤ 2 or e 2; .p e / D 2e 2 if p D 2 and e 3: In particular, if d j n, then .d / j .n/. Now, assume we are given n, along with a non-zero multiple f of .n/. We would like to calculate the complete prime factorization of n. We may proceed recursively: first, if n D 1, we may obviously halt; otherwise, we test if n is prime, using an efficient primality test, and if so, halt (if we are using the Miller–Rabin test, then we may erroneously halt even when n is composite, but we can ensure that this happens with negligible probability); otherwise, we split n as n D d1 d2 , using an algorithm to be described below, and then recursively factor both d1 and d2 ; since .d1 / j f and .d2 / j f , we may use the same value f in the recursion. So let us assume that n > 1 and n is not prime, and our goal now is to use f to obtain a non-trivial factorization of n. If n is even, then we can certainly do this. Moreover, if n is a perfect power, that is, if n D ab for integers a > 1 and b > 1, we can also obtain a non-trivial factorization of n (see Exercise 3.31). So let us assume not only that n > 1 and n is not prime, but also that n is odd, and n is not a perfect power. Let f D t 2h , where t is odd. Consider the following probabilistic algorithm:
10.4 Factoring and computing Euler’s phi function
321
˛ R ZC n d gcd.rep.˛/; n/ if d ¤ 1 then output d and halt ˇ ˛t for j 0 to h 1 do 0 d gcd.rep.ˇ/ C 1; n/ if d 0 … f1; ng then output d 0 and halt ˇ ˇ2 output “failure” We want to show that this algorithm outputs a non-trivial factor of n with probability at least 1=2. To do this, suppose the prime factorization of n is as in (10.13). Then by our assumptions about n, we have r 2 and each pi is odd. Let .piei / D ti 2hi , where ti is odd, for i D 1; : : : ; r, and let g WD maxfh1 ; : : : ; hr g. Note that since .n/ j f , we have 1 g h. Let be the .t2g 1 /-power map on Zn , and let G WD 1 .f˙1g/. As above, we shall show that G ¨ Zn , and if the algorithm chooses ˛ … G, then it splits n, which will prove that the algorithm splits n with probability at least 1=2. Let W Zn ! Zpe1 Zprer 1
be the ring isomorphism of the Chinese remainder theorem. We have .Im / D G1 Gr ; where Gi WD Zpi ei
t 2g
1
for i D 1; : : : ; r:
Let us assume the pi ’s are ordered so that hi D g for i D 1; : : : ; r 0 , and hi < g for i D r 0 C 1; : : : ; r, where we have 1 r 0 r. Then we have Gi D f˙1g for i D 1; : : : ; r 0 , and Gi D f1g for i D r 0 C 1; : : : ; r. By the observations in the previous paragraph, and the fact that r 2, the image of contains elements other than ˙1; for example, 1 . 1; 1; : : : ; 1/ is such an element. This means that G ¨ Zn . Suppose the algorithm chooses ˛ 2 ZC n n G. We want to show that n gets split. If ˛ … Zn , then gcd.rep.˛/; n/ is a non-trivial divisor of n, and so the algorithm certainly splits n. So assume ˛ 2 Zn nG. In loop iteration g 1, the value of ˇ is equal to .˛/, and writing .ˇ/ D .ˇ1 ; : : : ; ˇr /, we have ˇi D ˙1 for i D 1; : : : ; r. Let S be the set of indices i such that ˇi D 1.
322
Probabilistic primality testing
As ˛ … G, we know that ˇ ¤ ˙1, and so ; ¨ S ¨ f1; : : : ; rg. Thus, Y e gcd.rep.ˇ/ C 1; n/ D pi i i2S
is a non-trivial factor of n. This means that the algorithm splits n in loop iteration g 1 (if not in some earlier loop iteration). So we have shown that the above algorithm splits n with probability 1=2. If we iterate the algorithm until n gets split, the expected number of loop iterations required will be at most 2. Combining this with the above recursive algorithm, we get an algorithm that completely factors an arbitrary n in expected polynomial time. E XERCISE 10.12. Suppose you are given an integer n of the form n D pq, where p and q are distinct, `-bit primes, with p D 2p 0 C 1 and q D 2q 0 C 1, where p 0 and q 0 are themselves prime. Suppose that you are also given an integer t such that gcd.t; p 0 q 0 / ¤ 1. Show how to efficiently factor n. E XERCISE 10.13. Suppose there is a probabilistic algorithm A that takes as input an integer n of the form n D pq, where p and q are distinct, `-bit primes, with p D 2p 0 C 1 and q D 2q 0 C 1, where p 0 and q 0 are prime. The algorithm also takes as input ˛; ˇ 2 .Zn /2 . It outputs either “failure,” or integers x; y, not both zero, such that ˛ x ˇ y D 1. Furthermore, assume that A runs in expected polynomial time, and that for all n of the above form, and for randomly chosen ˛; ˇ 2 .Zn /2 , A succeeds in finding x; y as above with probability .n/. Here, the probability is taken over the random choice of ˛ and ˇ, as well as the random choices made during the execution of A on input .n; ˛; ˇ/. Show how to use A to construct another probabilistic algorithm A0 that takes as input n as above, runs in expected polynomial time, and that satisfies the following property: if .n/ 0:001, then A0 factors n with probability at least 0:999. 10.5 Notes The Miller–Rabin test is due to Miller [65] and Rabin [77]. The paper by Miller defined the set L0n , but did not give a probabilistic analysis. Rather, Miller showed that under a generalization of the Riemann hypothesis, for composite n, the least positive integer a such that Œan 2 Zn n L0n is at most O..log n/2 /, thus giving rise to a deterministic primality test whose correctness depends on the above unproved hypothesis. The later paper by Rabin re-interprets Miller’s result in the context of probabilistic algorithms.
10.5 Notes
323
Bach [10] gives an explicit version of Miller’s result, showing that under the same assumptions, the least positive integer a such that Œan 2 Zn n L0n is at most 2.log n/2 ; more generally, Bach shows that the following holds under a generalization of the Riemann hypothesis: For every positive integer n, and every proper subgroup G ¨ Zn , the least positive integer a such that Œan 2 Zn n G is at most 2.log n/2 , and the least positive integer b such that Œbn 2 Zn n G is at most 3.log n/2 . The first efficient probabilistic primality test was invented by Solovay and Strassen [97] (their paper was actually submitted for publication in 1974). Later, in Chapter 21, we shall discuss a recently discovered, deterministic, polynomial-time (though not very practical) primality test, whose analysis does not rely on any unproved hypothesis. Carmichael numbers are named after R. D. Carmichael, who was the first to discuss them, in work published in the early 20th century. Alford, Granville, and Pomerance [7] proved that there are infinitely many Carmichael numbers. Exercise 10.4 is based on Lehmann [57]. Theorem 10.4, as well as the table of values just below it, are from Kim and Pomerance [54]. In fact, these bounds hold for the weaker test based on Ln . Our analysis in §10.3.2 is loosely based on a similar analysis in §4.1 of Maurer [63]. Theorem 10.5 and its generalization in Exercise 10.9 are certainly not the best results possible in this area. The general goal of “sieve theory” is to prove useful upper and lower bounds for quantities like Rf .x; y/ that hold when y is as large as possible with respect to x. For example, using a technique known as Brun’s pure p sieve, one can show that for log y < log x, there exist ˇ and ˇ 0 , both of absolute value at most 1, such that p Y p Rf .x; y/ D .1 C ˇe log x /x .1 !f .p/=p/ C ˇ 0 x: py
Thus, this gives us very sharp estimates for Rf .x; y/ when x tends to infinity, and y is bounded by any fixed polynomial in log x. For a proof of this result, see §2.2 of Halberstam and Richert [43] (the result itself is stated as equation 2.16). Brun’s pure sieve is really just the first non-trivial sieve result, developed in the early 20th century; even stronger results, extending the useful range of y (but with larger error terms), have subsequently been proved. Theorem 10.6, as well as the table of values immediately below it, are from Damgård, Landrock, and Pomerance [32]. The algorithm presented in §10.4 for factoring an integer given a multiple of .n/ (or, for that matter, .n/) is essentially due to Miller [65]. However, just as for
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his primality test, Miller presents his algorithm as a deterministic algorithm, which he analyzes under a generalization of the Riemann hypothesis. The probabilistic version of Miller’s factoring algorithm appears to be “folklore.”
11 Finding generators and discrete logarithms in Zp
As we have seen in Theorem 7.28, for a prime p, Zp is a cyclic group of order p 1. This means that there exists a generator 2 Zp , such that each ˛ 2 Zp can be written uniquely as ˛ D x , where x is an integer with 0 x < p 1; the integer x is called the discrete logarithm of ˛ to the base , and is denoted log ˛. This chapter discusses some computational problems in this setting; namely, how to efficiently find a generator , and given and ˛, how to compute log ˛. More generally, if generates a subgroup G of Zp of order q, where q j .p 1/, and ˛ 2 G, then log ˛ is defined to be the unique integer x with 0 x < q and ˛ D x . In some situations it is more convenient to view log ˛ as an element of Zq . Also for x 2 Zq , with x D Œaq , one may write x to denote a . There can 0 be no confusion, since if x D Œa0 q , then a D a . However, in this chapter, we shall view log ˛ as an integer. Although we work in the group Zp , all of the algorithms discussed in this chapter trivially generalize to any finite cyclic group that has a suitably compact representation of group elements and an efficient algorithm for performing the group operation on these representations. 11.1 Finding a generator for Zp There is no efficient algorithm known for this problem, unless the prime factorization of p 1 is given, and even then, we must resort to the use of a probabilistic algorithm. Of course, factoring in general is believed to be a very difficult problem, so it may not be easy to get the prime factorization of p 1. However, if our goal is to construct a large prime p, together with a generator for Zp , then we may use Algorithm RFN in §9.6 to generate a random factored number n in some range, test n C 1 for primality, and then repeat until we get a factored number n such that p D n C 1 is prime. In this way, we can generate a random prime p in a given range along with the factorization of p 1. 325
Finding generators and discrete logarithms in Zp
326
We now present an efficient probabilistic algorithm that takes as input an odd prime p, along with the prime factorization p
1D
r Y
qiei ;
i D1
and outputs a generator for for i
Zp .
It runs as follows:
1 to r do repeat choose ˛ 2 Zp at random compute ˇ ˛ .p 1/=qi until ˇ ¤ 1
i Qr
˛ .p
e
1/=qi i
i D1 i output First, let us analyze the correctness of this algorithm. When the i th loop iteration terminates, by construction, we have q
ei
q
ei
i i D 1 but i i
1
¤ 1:
It follows (see Theorem 6.37) that i has multiplicative order qiei . From this, it follows (see Theorem 6.38) that has multiplicative order p 1. Thus, we have shown that if the algorithm terminates, its output is always correct. Let us now analyze the running time of this algorithm. Fix i D 1; : : : ; r, and consider the repeat/until loop in the i th iteration of the outer loop. Let Li be the random variable whose value is the number of iterations of this repeat/until loop. Since ˛ is chosen at random from Zp , the value of ˇ is uniformly distributed over the image of the .p 1/=qi -power map (see Theorem 8.5), and since the latter is a subgroup of Zp of order qi (see Example 7.61), we see that ˇ D 1 with probability 1=qi . Thus, Li has a geometric distribution with associated success probability 1 1=qi , and EŒLi D 1=.1 1=qi / 2 (see Theorem 9.3). Now set L WD L1 C C Lr . By linearity of expectation (Theorem 8.14), we have EŒL D EŒL1 C C EŒLr 2r. The running time Z of the entire algorithm is O.L len.p/3 /, and hence the expected running is EŒZ D O.r len.p/3 /, and since r log2 p, we have EŒZ D O.len.p/4 /. Although this algorithm is quite practical, there are asymptotically faster algorithms for this problem (see Exercise 11.2). E XERCISE 11.1. Suppose we are not given the prime factorization of p
1, but
11.2 Computing discrete logarithms in Zp
327
rather, just a prime q dividing p 1, and we want to find an element of multiplicative order q in Zp . Design and analyze an efficient algorithm to do this. E XERCISE 11.2. Suppose we are given a prime p, along with the prime factorizaQ tion p 1 D riD1 qiei . (a) If, in addition, we are given ˛ 2 Zp , show how to compute the multiplicative order of ˛ in time O.r len.p/3 /. Hint: use Exercise 6.40. (b) Improve the running time bound to O.len.r/ len.p/3 /. Hint: use Exercise 3.39. (c) Modifying the algorithm you developed for part (b), show how to construct a generator for Zp in expected time O.len.r/ len.p/3 /. E XERCISE 11.3. Suppose we are given a positive integer n, along with its prime factorization n D p1e1 prer , and that for each i D 1; : : : ; r, we are also given the prime factorization of pi 1. Show how to efficiently compute the multiplicative order of any element ˛ 2 Zn . E XERCISE 11.4. Suppose there is an efficient algorithm that takes as input a positive integer n and an element ˛ 2 Zn , and computes the multiplicative order of ˛. Show how to use this algorithm to build an efficient integer factoring algorithm. 11.2 Computing discrete logarithms in Zp In this section, we consider algorithms for computing the discrete logarithm of ˛ 2 Zp to a given base . The algorithms we present here are, in the worst case, exponential-time algorithms, and are by no means the best possible; however, in some special cases, these algorithms are not so bad. 11.2.1 Brute-force search Zp
Suppose that 2 generates a subgroup G of Zp of order q > 1 (not necessarily prime), and we are given p, q, , and ˛ 2 G, and wish to compute log ˛. The simplest algorithm to solve the problem is brute-force search: ˇ 1 i 0 while ˇ ¤ ˛ do ˇ ˇ i i C1 output i This algorithm is clearly correct, and the main loop will always halt after at
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Finding generators and discrete logarithms in Zp
most q iterations (assuming, as we are, that ˛ 2 G). So the total running time is O.q len.p/2 /. 11.2.2 Baby step/giant step method As above, suppose that 2 Zp generates a subgroup G of Zp of order q > 1 (not necessarily prime), and we are given p, q, , and ˛ 2 G, and wish to compute log ˛. A faster algorithm than brute-force search is the baby step/giant step method. It works as follows. Let us choose an approximation m to q 1=2 . It does not have to be a very good approximation — we just need m D ‚.q 1=2 /. Also, let m0 D bq=mc, so that m0 D ‚.q 1=2 / as well. The idea is to compute all the values i for i D 0; : : : ; m 1 (the “baby steps”) and to build an “associative array” (or “lookup table”) T that maps the key i to the value i . For ˇ 2 Zp , we shall write T Œˇ to denote the value associated with the key ˇ, writing T Œˇ D ? if there is no such value. We shall assume that T is implemented so that accessing T Œˇ is fast. Using an appropriate data structure, T can be implemented so that accessing individual elements takes time O.len.p//. One such data structure is a radix tree (also called a search trie). Other data structures may be used (for example, a hash table or a binary search tree), but these may have somewhat different access times. We can build the associative array T using the following algorithm: initialize T // T Œˇ D ? for all ˇ 2 Zp ˇ 1 for i 0 to m 1 do T Œˇ i ˇ ˇ Clearly, this algorithm takes time O.q 1=2 len.p/2 /. After building the lookup table, we execute the following procedure (the “giant steps”):
0
m ˇ ˛; j 0; i while i D ? do ˇ ˇ 0; j
T Œˇ j C 1; i
T Œˇ
x jm C i output x To analyze this procedure, suppose that ˛ D x with 0 x < q. Now, x
11.2 Computing discrete logarithms in Zp
329
can be written in a unique way as x D vm C u, where u and v are integers with 0 u < m and 0 v m0 . In the j th loop iteration, for j D 0; 1; : : : ; we have ˇ D ˛
mj
D .v
j /mCu
:
So we will detect i ¤ ? precisely when j D v, in which case i D u. Thus, the output will be correct, and the total running time of the algorithm (for both the “baby steps” and “giant steps” parts) is easily seen to be O.q 1=2 len.p/2 /. While this algorithm is much faster than brute-force search, it has the drawback that it requires space for about q 1=2 elements of Zp . Of course, there is a “time/space trade-off” here: by choosing m smaller, we get a table of size O.m/, but the running time will be proportional to O.q=m/. In §11.2.5 below, we discuss an algorithm that runs (at least heuristically) in time O.q 1=2 len.q/ len.p/2 /, but which requires space for only a constant number of elements of Zp . 11.2.3 Groups of order q e Suppose that 2 Zp generates a subgroup G of Zp of order q e , where q > 1 and e 1, and we are given p, q, e, , and ˛ 2 G, and wish to compute log ˛. There is a simple algorithm that allows one to reduce this problem to the problem of computing discrete logarithms in the subgroup of Zp of order q. It is perhaps easiest to describe the algorithm recursively. The base case is when e D 1, in which case, we use an algorithm for the subgroup of Zp of order q. For this, we might employ the algorithm in §11.2.2, or if q is very small, the algorithm in §11.2.1. Suppose now that e > 1. We choose an integer f with 0 < f < e. Different strategies for choosing f yield different algorithms — we discuss this below. Suppose ˛ D x , where 0 x < q e . Then we can write x D q f v C u, where u and v are integers with 0 u < q f and 0 v < q e f . Therefore, ˛q
e f
D q
e f
u
:
e f
Note that q has multiplicative order q f , and so if we recursively compute the e f e f discrete logarithm of ˛ q to the base q , we obtain u. Having obtained u, observe that ˛= u D q qf
f
v
:
Note also that has multiplicative order q e f , and so if we recursively compute f the discrete logarithm of ˛= u to the base q , we obtain v, from which we then compute x D q f v C u.
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Finding generators and discrete logarithms in Zp
Let us put together the above ideas succinctly in a recursive procedure: Algorithm RDL. On input p; q; e; ; ˛ as above, do the following: if e D 1 then return log ˛ // base case: use a different algorithm else select f 2 f1; : : : ; e 1g e f e f u RDL.p; q; f; q ; ˛q / // 0 u < q f f v RDL.p; q; e f; q ; ˛= u / // 0 v < q e f return q f v C u To analyze the running time of this recursive algorithm, note that the running time of the body of one recursive invocation (not counting the running time of the recursive calls it makes) is O.e len.q/ len.p/2 /. To calculate the total running time, we have to sum up the running times of all the recursive calls plus the running times of all the base cases. Regardless of the strategy for choosing f , the total number of base case invocations is e. Note that all the base cases compute discrete logarithms to the e 1 base q . Assuming we implement the base case using the baby step/giant step algorithm in §11.2.2, the total running time for all the base cases is therefore O.eq 1=2 len.p/2 /. The total running time for the recursion (not including the base case computations) depends on the strategy used to choose the split f . It is helpful to represent the behavior of the algorithm using a recursion tree. This is a binary tree, where every node represents one recursive invocation of the algorithm; the root of the tree represents the initial invocation of the algorithm; for every node N in the tree, if N represents the recursive invocation I , then N ’s children (if any) represent the recursive invocations made by I . We can naturally organize the nodes of the recursion tree by levels: the root of the recursion tree is at level 0, its children are at level 1, its grandchildren at level 2, and so on. The depth of the recursion tree is defined to be the maximum level of any node. We consider two different strategies for choosing the split f : If we always choose f D 1 or f D e 1, then the depth of the recursion tree is O.e/. The running time contributed by each level of the recursion tree is O.e len.q/ len.p/2 /, and so the total running time for the recursion is O.e 2 len.q/ len.p/2 /. Note that if f D 1, then the algorithm is essentially tail recursive, and so may be easily converted to an iterative algorithm without the need for a stack. If we use a “balanced” divide-and-conquer strategy, choosing f e=2, then the depth of the recursion tree is O.len.e//, while the
11.2 Computing discrete logarithms in Zp
331
running time contributed by each level of the recursion tree is still O.e len.q/ len.p/2 /. It follows that the total running time of the recursion is O.e len.e/ len.q/ len.p/2 /. Assuming we use the faster, balanced recursion strategy, and that we use the baby step/giant step algorithm for the base case, the total running time of Algorithm RDL is: O..eq 1=2 C e len.e/ len.q// len.p/2 /: 11.2.4 Discrete logarithms in Zp Suppose that we are given a prime p, along with the prime factorization p
1D
r Y
qiei ;
i D1
Zp ,
Zp .
a generator for and ˛ 2 We wish to compute log ˛. Suppose that ˛ D x , where 0 x < p 1. Then for i D 1; : : : ; r, we have ei ei x ˛ .p 1/=qi D .p 1/=qi : Note that .p
e
1/=qi i e 1/=qi i
has multiplicative order qiei , and if xi is the discrete logei
arithm of ˛ .p to the base .p 1/=qi , then we have 0 xi < qiei and x xi .mod qiei /. Thus, if we compute the values x1 ; : : : ; xr , using the algorithm in §11.2.3, we can obtain x using the algorithm of the Chinese remainder theorem (see Theorem 4.5). If we define q WD maxfq1 ; : : : ; qr g, then the running time of this algorithm will be bounded by q 1=2 len.p/O.1/ . We conclude that the difficulty of computing discrete logarithms in Zp is determined by the size of the largest prime dividing p 1. 11.2.5 A space-efficient square-root time algorithm We present a more space-efficient alternative to the algorithm in §11.2.2, the analysis of which we leave as a series of exercises for the reader. The algorithm makes a somewhat heuristic assumption that we have a function that “behaves” for all practical purposes like a random function. Such functions can indeed be constructed using cryptographic techniques under reasonable intractability assumptions; however, for the particular application here, one can get by in practice with much simpler constructions.
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Finding generators and discrete logarithms in Zp
Let p be a prime, q a prime dividing p 1, an element of Zp that generates a subgroup G of Zp of order q, and ˛ 2 G. Let F be a function mapping elements of G to f0; : : : ; q 1g. Define H W G ! G to be the function that sends ˇ to ˇ˛ F .ˇ / . The algorithm runs as follows: i 1 x 0, ˇ ˛, 0 x F .ˇ/, ˇ 0 H.ˇ/ while ˇ ¤ ˇ 0 do x .x C F .ˇ// mod q, ˇ H.ˇ/ x0 .x 0 C F .ˇ 0 // mod q, ˇ 0 H.ˇ 0 / x0 .x 0 C F .ˇ 0 // mod q, ˇ 0 H.ˇ 0 / i i C1 if i < q then output .x x 0 /i 1 mod q else output “fail” To analyze this algorithm, let us define ˇ1 ; ˇ2 ; : : : ; as follows: ˇ1 WD ˛ and for i > 1, ˇi WD H.ˇi 1 /. E XERCISE 11.5. Show that each time the main loop of the algorithm is entered, 0 we have ˇ D ˇi D x ˛ i , and ˇ 0 D ˇ2i D x ˛ 2i . E XERCISE 11.6. Show that if the loop terminates with i < q, the value output is equal to log ˛. E XERCISE 11.7. Let j be the smallest index such that ˇj D ˇk for some index k < j . Show that j q C 1 and that the loop terminates with i < j (and in particular, i q). E XERCISE 11.8. Assume that F is a random function, meaning that it is chosen at random, uniformly from among all functions from G into f0; : : : ; q 1g. Show that this implies that H is a random function, meaning that it is uniformly distributed over all functions from G into G. E XERCISE 11.9. Assuming that F is a random function as in the previous exercise, apply the result of Exercise 8.39 to conclude that the expected running time of the algorithm is O.q 1=2 len.q/ len.p/2 /, and that the probability that the algorithm fails is exponentially small in q.
11.3 The Diffie–Hellman key establishment protocol
333
11.3 The Diffie–Hellman key establishment protocol One of the main motivations for studying algorithms for computing discrete logarithms is the relation between this problem and the problem of breaking a protocol called the Diffie–Hellman key establishment protocol, named after its inventors. In this protocol, Alice and Bob need never to have talked to each other before, but nevertheless, can establish a shared secret key that nobody else can easily compute. To use this protocol, a third party must provide a “telephone book,” which contains the following information: p, q, and , where p and q are primes with q j .p generating a subgroup G of Zp of order q;
1/, and is an element
an entry for each user, such as Alice or Bob, that contains the user’s name, along with a “public key” for that user, which is an element of the group G. To use this system, Alice posts her public key in the telephone book, which is of the form ˛ D x , where x 2 f0; : : : ; q 1g is chosen by Alice at random. The value of x is Alice’s “secret key,” which Alice never divulges to anybody. Likewise, Bob posts his public key, which is of the form ˇ D y , where y 2 f0; : : : ; q 1g is chosen by Bob at random, and is his secret key. To establish a shared key known only between them, Alice retrieves Bob’s public key ˇ from the telephone book, and computes A WD ˇ x . Likewise, Bob retrieves Alice’s public key ˛, and computes B WD ˛ y . It is easy to see that A D ˇ x D . y /x D xy D . x /y D ˛ y D B ; and hence Alice and Bob share the same secret key WD A D B . Using this shared secret key, they can then use standard methods for encryption and message authentication to hold a secure conversation. We shall not go any further into how this is done; rather, we briefly (and only superficially) discuss some aspects of the security of the key establishment protocol itself. Clearly, if an attacker obtains ˛ and ˇ from the telephone book, and computes x D log ˛, then he can compute Alice and Bob’s shared key as D ˇ x — in fact, given x, an attacker can efficiently compute any key shared between Alice and another user. Thus, if this system is to be secure, it should be very difficult to compute discrete logarithms. However, the assumption that computing discrete logarithms is hard is not enough to guarantee security. Indeed, it is not entirely inconceivable that the discrete logarithm problem is hard, and yet the problem of computing from ˛ and ˇ is easy. The latter problem — computing from ˛ and ˇ — is called the Diffie–Hellman problem. As in the discussion of the RSA cryptosystem in §4.7, the reader is warned that the above discussion about security is a bit of an oversimplification. A complete
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Finding generators and discrete logarithms in Zp
discussion of all the security issues related to the above protocol is beyond the scope of this text. Note that in our presentation of the Diffie–Hellman protocol, we work with a generator of a subgroup G of Zp of prime order, rather than a generator for Zp . There are several reasons for doing this: one is that there are no known discrete logarithm algorithms that are any more practical in G than in Zp , provided the order q of G is sufficiently large; another is that by working in G, the protocol becomes substantially more efficient. In typical implementations, p is 1024 bits long, so as to protect against subexponential-time algorithms such as those discussed later in §15.2, while q is 160 bits long, which is enough to protect against the square-roottime algorithms discussed in §11.2.2 and §11.2.5. The modular exponentiations in the protocol will run several times faster using “short,” 160-bit exponents rather than “long,” 1024-bit exponents. For the following exercise, we need the following notions from complexity theory. We say problem A is deterministic poly-time reducible to problem B if there exists a deterministic algorithm R for solving problem A that makes calls to a subroutine for problem B, where the running time of R (not including the running time for the subroutine for B) is polynomial in the input length. We say that A and B are deterministic poly-time equivalent if A is deterministic poly-time reducible to B and B is deterministic poly-time reducible to A. E XERCISE 11.10. Consider the following problems. (a) Given a prime p, a prime q that divides p 1, an element 2 Zp generating a subgroup G of Zp of order q, and two elements ˛; ˇ 2 G, compute xy , where x WD log ˛ and y WD log ˇ. (This is just the Diffie–Hellman problem.) (b) Given a prime p, a prime q that divides p 1, an element 2 Zp generating 2
a subgroup G of Zp of order q, and an element ˛ 2 G, compute x , where x WD log ˛. (c) Given a prime p, a prime q that divides p 1, an element 2 Zp generating a subgroup G of Zp of order q, and two elements ˛; ˇ 2 G, with ˇ ¤ 1, 0 compute xy , where x WD log ˛, y 0 WD y 1 mod q, and y WD log ˇ. (d) Given a prime p, a prime q that divides p 1, an element 2 Zp generating a subgroup G of Zp of order q, and an element ˛ 2 G, with ˛ ¤ 1, 0 compute x , where x 0 WD x 1 mod q and x WD log ˛.
11.3 The Diffie–Hellman key establishment protocol
335
Show that these problems are deterministic poly-time equivalent. Moreover, your reductions should preserve the values of p, q, and ; that is, if the algorithm that reduces one problem to another takes as input an instance of the former problem of the form .p; q; ; : : :/, it should invoke the subroutine for the latter problem with inputs of the form .p; q; ; : : :/. E XERCISE 11.11. Suppose there is a probabilistic algorithm A that takes as input a prime p, a prime q that divides p 1, and an element 2 Zp generating a subgroup G of Zp of order q. The algorithm also takes as input ˛ 2 G. It outputs either “failure,” or log ˛. Furthermore, assume that A runs in expected polynomial time, and that for all p, q, and of the above form, and for randomly chosen ˛ 2 G, A succeeds in computing log ˛ with probability .p; q; /. Here, the probability is taken over the random choice of ˛, as well as the random choices made during the execution of A. Show how to use A to construct another probabilistic algorithm A0 that takes as input p, q, and as above, as well as ˛ 2 G, runs in expected polynomial time, and that satisfies the following property: if .p; q; / 0:001, then for all ˛ 2 G, A0 computes log ˛ with probability at least 0:999. The algorithm A0 in the previous exercise is an example of a random selfreduction, that is, an algorithm that reduces the task of solving an arbitrary instance of a given problem to that of solving a random instance of the problem. Intuitively, the existence of such a reduction means that the problem is no harder in the worst case than on average. E XERCISE 11.12. Let p be a prime, q a prime that divides p 1, 2 Zp an element that generates a subgroup G of Zp of order q, and ˛ 2 G. For ı 2 G, a representation of ı with respect to and ˛ is a pair of integers .r; s/, with 0 r < q and 0 s < q, such that r ˛ s D ı. (a) Show that for every ı 2 G, there are precisely q representations .r; s/ of ı with respect to and ˛, and among these, there is precisely one with s D 0. (b) Show that given a representation .r; s/ of 1 with respect to and ˛ such that s ¤ 0, we can efficiently compute log ˛. (c) Show that given any ı 2 G, along with any two distinct representations of ı with respect to and ˛, we can efficiently compute log ˛. (d) Suppose we are given access to an “oracle” that, when presented with any ı 2 G, tells us some representation of ı with respect to and ˛. Show how to use this oracle to efficiently compute log ˛. The following two exercises examine the danger of the use of “short” exponents
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Finding generators and discrete logarithms in Zp
in discrete logarithm based cryptographic schemes that do not work with a group of prime order. E XERCISE 11.13. Let p be a prime and let p 1 D q1e1 qrer be the prime factorization of p 1. Let be a generator for Zp . Let y be a positive number, and let Qp .y/ be the product of all the prime powers qiei with qi y. Suppose you are given p, y, the primes qi dividing p 1 with qi y, along with , an element ˛ of Zp , and a bound x, O where x WD log ˛ < x. O Show how to compute x in time O p .y//1=2 / len.p/O.1/ : .y 1=2 C .x=Q E XERCISE 11.14. Continuing with the previous, let Qp0 .y/ denote the product of all the primes qi dividing p 1 with qi y. Note that Qp0 .y/ j Qp .y/. The goal of this exercise is to estimate the expected value of log Qp0 .y/, assuming p is a large, random prime. To this end, let R be a random variable that is uniformly distributed over all `-bit primes, and assume that y 2`=3 . Assuming Conjecture 5.22, show that asymptotically (as ` ! 1), we have EŒlog QR0 .y/ D log y C O.1/. The results of the previous two exercises caution against the use of “short” exponents in cryptographic schemes based on the discrete logarithm problem for Zp . For example, suppose that p is a random 1024-bit prime, and that for reasons of efficiency, one chooses xO 2160 , thinking that a method such as the baby step/giant step method would require 280 steps to recover x. However, if we choose y 280 , then the above analysis implies that Qp .y/ is at least 280 with a reasonable probability, in which case x=Q O p .y/ is at most 280 , and so we can in fact recover x in 240 steps (there are known methods find the primes up to y that divide p 1 quickly enough). While 280 may be not be a feasible number of steps, 240 may very well be. Of course, none of these issues arise if one works in a subgroup of Zp of large prime order, which is the recommended practice. An interesting fact about the Diffie–Hellman problem is that there is no known efficient algorithm to recognize a solution to the problem. Some cryptographic protocols actually rely on the apparent difficulty of this decision problem, which is called the decisional Diffie–Hellman problem. The following three exercises develop a random self-reducibility property for this decision problem. E XERCISE 11.15. Let p be a prime, q a prime dividing p 1, and an element of Zp that generates a subgroup G of order q. Let ˛ 2 G, and let H be the subgroup of G G generated by . ; ˛/. Let Q ; ˛Q be arbitrary elements of G, and define the map W
Zq Zq ! G G .Œrq ; Œsq / 7! . r Q s ; ˛ r ˛Q s /:
11.3 The Diffie–Hellman key establishment protocol
337
Show that the definition of is unambiguous, that is a group homomorphism, and that if . ; Q ˛/ Q 2 H , then Im D H , and if . ; Q ˛/ Q … H , then Im D G G. E XERCISE 11.16. For p; q; as in the previous exercise, let Dp;q; consist of all triples of the form . x ; y ; xy /, and let Rp;q; consist of all triples of the form . x ; y ; z /. Using the result from the previous exercise, design a probabilistic algorithm that runs in expected polynomial time, and that on input p; q; , along with a triple 2 Rp;q; , outputs a triple 2 Rp;q; such that if 2 Dp;q; , then is uniformly distributed over Dp;q; , and if … Dp;q; , then is uniformly distributed over Rp;q; . E XERCISE 11.17. Suppose that A is a probabilistic algorithm that takes as input p; q; as in the previous exercise, along with a triple 2 Rp;q; , and outputs either 0 or 1. Furthermore, assume that A runs in expected polynomial time. Define two random variables, Xp;q; and Yp;q; , as follows: Xp;q; is defined to be the output of A on input p; q; , and , where is uniformly distributed over Dp;q; , and Yp;q; is defined to be the output of A on input p; q; , and , where is uniformly distributed over Rp;q; . In both cases, the value of the random variable is determined by the random choice of , as well as the random choices made by the algorithm. Define ˇ ˇ ˇ ˇ ˇ ˇ WD .p; q; / ˇPŒXp;q; D 1 PŒYp;q; D 1ˇ: Using the result of the previous exercise, show how to use A to design a probabilistic, expected polynomial-time algorithm that takes as input p; q; as above, along with 2 Rp;q; , and outputs either “yes” or “no,” so that if .p; q; / 0:001, then for all 2 Rp;q; , the probability that A0 correctly determines whether 2 Dp;q; is at least 0:999. Hint: use the Chernoff bound. The following exercise demonstrates that distinguishing “Diffie–Hellman triples” from “random triples” is hard only if the order of the underlying group is not divisible by any small primes, which is another reason we have chosen to work with groups of large prime order. E XERCISE 11.18. Assume the notation of the previous exercise, but let us drop the restriction that q is prime. Design and analyze a deterministic algorithm A
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Finding generators and discrete logarithms in Zp
that takes inputs p; q; and 2 Rp;q; , that outputs 0 or 1, and that satisfies the following property: if t is the smallest prime dividing q, then A runs in time .t C len.p//O.1/ , and the “distinguishing advantage” .p; q; / for A on inputs p; q; is at least 1=t . 11.4 Notes The probabilistic algorithm in §11.1 for finding a generator for Zp can be made deterministic under a generalization of the Riemann hypothesis. Indeed, as discussed in §10.5, under such a hypothesis, Bach’s result [10] implies that for each prime q j .p 1/, the least positive integer a such that Œap 2 Zp n .Zp /q is at most 2 log p. Related to the problem of constructing a generator for Zp is the question of how big is the smallest positive integer g such that Œgp is a generator for Zp ; that is, how big is the smallest (positive) primitive root modulo p. The best bounds on the least primitive root are also obtained using the same generalization of the Riemann hypothesis mentioned above. Under this hypothesis, Wang [102] showed that the least primitive root modulo p is O.r 6 len.p/2 /, where r is the number of distinct prime divisors of p 1. Shoup [93] improved Wang’s bound to O.r 4 len.r/4 len.p/2 / by adapting a result of Iwaniec [49, 50] and applying it to Wang’s proof. The best unconditional bound on the smallest primitive root modulo p is p 1=4Co.1/ (this bound is also in Wang [102]). Of course, just because there exists a small primitive root, there is no known way to efficiently recognize a primitive root modulo p without knowing the prime factorization of p 1. As we already mentioned, all of the algorithms presented in this chapter are completely “generic,” in the sense that they work in any finite cyclic group — we really did not exploit any properties about Zp other than the fact that it is a cyclic group. In fact, as far as such “generic” algorithms go, the algorithms presented here for discrete logarithms are optimal [69, 96]. However, there are faster, “nongeneric” algorithms (though still not polynomial time) for discrete logarithms in Zp . We shall examine one such algorithm later, in Chapter 15. The “baby step/giant step” algorithm in §11.2.2 is due to Shanks [89]. See, for example, the book by Cormen, Leiserson, Rivest, and Stein [29] for appropriate data structures to implement the lookup table used in that algorithm. In particular, see Problem 12-2 in [29] for a brief introduction to radix trees, which is the data structure that yields the best running time (at least in principle) for our application. The algorithms in §11.2.3 and §11.2.4 are variants of an algorithm published by Pohlig and Hellman [73]. See Chapter 4 of [29] for details on how one analyzes recursive algorithms, such as the one presented in §11.2.3; in particular, Section 4.2 in [29] discusses in detail the notion of a recursion tree.
11.4 Notes
339
The algorithm in §11.2.5 is a variant of an algorithm of Pollard [74]; in fact, Pollard’s algorithm is a bit more efficient than the one presented here, but the analysis of its running time depends on stronger heuristics. Pollard’s paper also describes an algorithm for computing discrete logarithms that lie in a restricted interval — if the interval has width w, this algorithm runs (heuristically) in time w 1=2 len.p/O.1/ , and requires space for O.len.w// elements of Zp . This algorithm is useful in reducing the space requirement for the algorithm of Exercise 11.13. The key establishment protocol in §11.3 is from Diffie and Hellman [34]. That paper initiated the study of public key cryptography, which has proved to be a very rich field of research. Exercises 11.13 and 11.14 are based on van Oorschot and Wiener [72]. For more on the decisional Diffie–Hellman assumption, see Boneh [18].
12 Quadratic reciprocity and computing modular square roots
In §2.8, we initiated an investigation of quadratic residues. This chapter continues this investigation. Recall that an integer a is called a quadratic residue modulo a positive integer n if gcd.a; n/ D 1 and a b 2 .mod n/ for some integer b. First, we derive the famous law of quadratic reciprocity. This law, while historically important for reasons of pure mathematical interest, also has important computational applications, including a fast algorithm for testing if an integer is a quadratic residue modulo a prime. Second, we investigate the problem of computing modular square roots: given a quadratic residue a modulo n, compute an integer b such that a b 2 .mod n/. As we will see, there are efficient probabilistic algorithms for this problem when n is prime, and more generally, when the factorization of n into primes is known. 12.1 The Legendre symbol For an odd prime p and an integer a with gcd.a; p/ D 1, the Legendre symbol .a j p/ is defined to be 1 if a is a quadratic residue modulo p, and 1 otherwise. For completeness, one defines .a j p/ D 0 if p j a. The following theorem summarizes the essential properties of the Legendre symbol. Theorem 12.1. Let p be an odd prime, and let a; b 2 Z. Then we have (i) .a j p/ a.p
1/=2
.mod p/; in particular, . 1 j p/ D . 1/.p
1/=2 ;
(ii) .a j p/.b j p/ D .ab j p/; (iii) a b .mod p/ implies .a j p/ D .b j p/; (iv) .2 j p/ D . 1/.p
2
1/=8 ;
(v) if q is an odd prime, then .p j q/ D . 1/
p 1q 1 2 2
.q j p/:
Part (i) of the theorem is just a restatement of Euler’s criterion (Theorem 2.21).
340
341
12.1 The Legendre symbol
As was observed in Theorem 2.31, this implies that 1 is a quadratic residue modulo p if and only if p 1 .mod 4/. Thus, the quadratic residuosity of 1 modulo p is determined by the residue class of p modulo 4. Part (ii) of the theorem follows immediately from part (i), and part (iii) is an immediate consequence of the definition of the Legendre symbol. Part (iv), which we will prove below, can also be recast as saying that 2 is a quadratic residue modulo p if and only if p ˙1 .mod 8/. Thus, the quadratic residuosity of 2 modulo p is determined by the residue class of p modulo 8. Part (v), which we will also prove below, is the law of quadratic reciprocity. Note that when p D q, both .p j q/ and .q j p/ are zero, and so the statement of part (v) is trivially true — the interesting case is when p ¤ q, and in this case, part (v) is equivalent to saying that .p j q/.q j p/ D . 1/
p 1q 1 2 2
:
Thus, the Legendre symbols .p j q/ and .q j p/ have the same values if and only if either p 1 .mod 4/ or q 1 .mod 4/. As the following examples illustrate, this result also shows that for a given odd prime q, the quadratic residuosity of q modulo another odd prime p is determined by the residue class of p modulo either q or 4q. Example 12.1. Let us characterize those primes p modulo which 5 is a quadratic residue. Since 5 1 .mod 4/, the law of quadratic reciprocity tells us that .5 j p/ D .p j 5/. Now, among the numbers ˙1, ˙2, the quadratic residues modulo 5 are ˙1. It follows that 5 is a quadratic residue modulo p if and only if p ˙1 .mod 5/. This example obviously generalizes, replacing 5 by any prime q 1 .mod 4/, and replacing the above congruences modulo 5 by appropriate congruences modulo q. Example 12.2. Let us characterize those primes p modulo which 3 is a quadratic residue. Since 3 6 1 .mod 4/, we must be careful in our application of the law of quadratic reciprocity. First, suppose that p 1 .mod 4/. Then .3 j p/ D .p j 3/, and so 3 is a quadratic residue modulo p if and only if p 1 .mod 3/. Second, suppose that p 6 1 .mod 4/. Then .3 j p/ D .p j 3/, and so 3 is a quadratic residue modulo p if and only if p 1 .mod 3/. Putting this all together, we see that 3 is quadratic residue modulo p if and only if p 1 .mod 4/ and p 1 .mod 3/ or p
1 .mod 4/ and p
1 .mod 3/:
Using the Chinese remainder theorem, we can restate this criterion in terms of
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Quadratic reciprocity and computing modular square roots
residue classes modulo 12: 3 is quadratic residue modulo p if and only if p ˙1 .mod 12/. This example obviously generalizes, replacing 3 by any prime q 1 .mod 4/, and replacing the above congruences modulo 12 by appropriate congruences modulo 4q. The rest of this section is devoted to a proof of parts (iv) and (v) of Theorem 12.1. The proof is completely elementary, although a bit technical. Theorem 12.2 (Gauss’ lemma). Let p be an odd prime and let a be an integer not divisible by p. Define ˛j WD ja mod p for j D 1; : : : ; .p 1/=2, and let n be the number of indices j for which ˛j > p=2. Then .a j p/ D . 1/n . Proof. Let r1 ; : : : ; rn denote the values ˛j that exceed p=2, and let s1 ; : : : ; sk denote the remaining values ˛j . The ri and si are all distinct and non-zero. We have 0 < p ri < p=2 for i D 1; : : : ; n, and no p ri is an sj ; indeed, if p ri D sj , then sj ri .mod p/, and writing sj D ua mod p and ri D va mod p, for some u; v D 1; : : : ; .p 1/=2, we have ua va .mod p/, which implies u v .mod p/, which is impossible. It follows that the sequence of numbers s1 ; : : : ; sk ; p r1 ; : : : ; p rn is just a reordering of 1; : : : ; .p 1/=2. Then we have ..p
1/=2/Š s1 sk . r1 / . rn / . 1/n s1 sk r1 rn . 1/n ..p
1/=2/Š a.p
1/=2
.mod p/;
and canceling the factor ..p 1/=2/Š, we obtain a.p 1/=2 . 1/n .mod p/, and the result follows from the fact that .a j p/ a.p 1/=2 .mod p/. Theorem 12.3. If p is an odd prime and gcd.a; 2p/ D 1, then .a j p/ D . 1/t P.p 1/=2 2 where t D j D1 bja=pc. Also, .2 j p/ D . 1/.p 1/=8 . Proof. Let a be an integer not divisible by p, but which may be even, and let us adopt the same notation as in the statement and proof of Theorem 12.2; in particular, ˛1 ; : : : ; ˛.p 1/=2 , r1 ; : : : ; rn , and s1 ; : : : ; sk are as defined there. Note that ja D pbja=pc C ˛j , for j D 1; : : : ; .p 1/=2, so we have .pX 1/=2 j D1
ja D
.pX 1/=2 j D1
pbja=pc C
n X j D1
rj C
k X
sj :
(12.1)
j D1
Also, we saw in the proof of Theorem 12.2 that the integers s1 ; : : : ; sk ; p
343
12.1 The Legendre symbol
r1 ; : : : ; p
rn are a reordering of 1; : : : ; .p
.pX 1/=2
j D
j D1
n X
rj / C
.p
j D1
k X
1/=2, and hence n X
sj D np
j D1
rj C
j D1
k X
sj :
(12.2)
j D1
Subtracting (12.2) from (12.1), we get .a
1/
.pX 1/=2
j Dp
.pX 1/=2
j D1
bja=pc
n X n C2 rj :
j D1
(12.3)
j D1
Note that .pX 1/=2
j D
p2
1 8
j D1
;
(12.4)
which together with (12.3) implies .a
1/
p2
1 8
.pX 1/=2
bja=pc
n .mod 2/:
(12.5)
j D1
If a is odd, (12.5) implies n
.pX 1/=2
bja=pc .mod 2/:
(12.6)
j D1
If a D 2, then b2j=pc D 0 for j D 1; : : : ; .p n
p2
1/=2, and (12.5) implies
1
.mod 2/: (12.7) 8 The theorem now follows from (12.6) and (12.7), together with Theorem 12.2. Note that this last theorem proves part (iv) of Theorem 12.1. The next theorem proves part (v). Theorem 12.4. If p and q are distinct odd primes, then .p j q/.q j p/ D . 1/
p 1q 1 2 2
:
Proof. Let S be the set of pairs of integers .x; y/ with 1 x .p 1/=2 and 1 y .q 1/=2. Note that S contains no pair .x; y/ with qx D py, so let us partition S into two subsets: S1 contains all pairs .x; y/ with qx > py, and S2 contains all pairs .x; y/ with qx < py. Note that .x; y/ 2 S1 if and only if
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Quadratic reciprocity and computing modular square roots
1 x .p 1/=2 and 1 y bqx=pc. So jS1 j D P.q 1/=2 jS2 j D yD1 bpy=qc: So we have 1q
p 2
1 2
D jS j D jS1 j C jS2 j D
.pX 1/=2
P.p
1/=2 bqx=pc: Similarly, xD1
bqx=pc C
xD1
.qX 1/=2
bpy=qc;
yD1
and Theorem 12.3 implies .p j q/.q j p/ D . 1/
p 1q 1 2 2
:
E XERCISE 12.1. Characterize those odd primes p for which .15 j p/ D 1, in terms of the residue class of p modulo 60. E XERCISE 12.2. Let p be an odd prime. Show that the following are equivalent: (a) . 2 j p/ D 1; (b) p 1 or 3 .mod 8/; (c) p D r 2 C 2t 2 for some r; t 2 Z. 12.2 The Jacobi symbol Let a; n be integers, where n is positive and odd, so that n D q1 qk , where the qi are odd primes, not necessarily distinct. Then the Jacobi symbol .a j n/ is defined as .a j n/ WD .a j q1 / .a j qk /; where .a j qj / is the Legendre symbol. By definition, .a j 1/ D 1 for all a 2 Z. Thus, the Jacobi symbol essentially extends the domain of definition of the Legendre symbol. Note that .a j n/ 2 f0; ˙1g, and that .a j n/ D 0 if and only if gcd.a; n/ > 1. The following theorem summarizes the essential properties of the Jacobi symbol. Theorem 12.5. Let m; n be odd, positive integers, and let a; b 2 Z. Then (i) .ab j n/ D .a j n/.b j n/; (ii) .a j mn/ D .a j m/.a j n/; (iii) a b .mod n/ implies .a j n/ D .b j n/; (iv) . 1 j n/ D . 1/.n .n2
(v) .2 j n/ D . 1/
(vi) .m j n/ D . 1/
1/=2 ; 1/=8 ;
m 1n 1 2 2
.n j m/:
12.2 The Jacobi symbol
345
Proof. Parts (i)–(iii) follow directly from the definition (exercise). For parts (iv) and (vi), one can easily verify (exercise) that for odd integers n1 ; : : : ; n k , k X .ni
1/=2 .n1 nk
1/=2 .mod 2/:
i D1
Part (iv) easily follows from this fact, along with part (ii) of this theorem and part (i) of Theorem 12.1 (exercise). Part (vi) easily follows from this fact, along with parts (i) and (ii) of this theorem, and part (v) of Theorem 12.1 (exercise). For part (v), one can easily verify (exercise) that for odd integers n1 ; : : : ; nk , k X
.n2i
1/=8 .n21 n2k
1/=8 .mod 2/:
i D1
Part (v) easily follows from this fact, along with part (ii) of this theorem, and part (iv) of Theorem 12.1 (exercise). As we shall see later, this theorem is extremely useful from a computational point of view — with it, one can efficiently compute .a j n/, without having to know the prime factorization of either a or n. Also, in applying this theorem it is useful to observe that for odd integers m; n, . 1/.n . 1/ .
.n2
1/=2
D 1 ” n 1 .mod 4/;
1/=8
D 1 ” n ˙1 .mod 8/;
1/..m 1/=2/..n 1/=2/
D 1 ” m 1 .mod 4/ or n 1 .mod 4/.
Suppose a is a quadratic residue modulo n, so that a b 2 .mod n/, where gcd.a; n/ D 1 D gcd.b; n/. Then by parts (iii) and (i) of Theorem 12.5, we have .a j n/ D .b 2 j n/ D .b j n/2 D 1. Thus, if a is a quadratic residue modulo n, then .a j n/ D 1. The converse, however, does not hold: .a j n/ D 1 does not imply that a is a quadratic residue modulo n (see Exercise 12.3 below). It is sometimes useful to view the Jacobi symbol as a group homomorphism. Let n be an odd, positive integer. Define the Jacobi map Jn W
Zn ! f˙1g Œan 7! .a j n/:
First, we note that by part (iii) of Theorem 12.5, this definition is unambiguous. Second, we note that since gcd.a; n/ D 1 implies .a j n/ D ˙1, the image of Jn is indeed contained in f˙1g. Third, we note that by part (i) of Theorem 12.5, Jn is a group homomorphism. Since Jn is a group homomorphism, it follows that its kernel, Ker Jn , is a subgroup of Zn .
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Quadratic reciprocity and computing modular square roots
E XERCISE 12.3. Let n be an odd, positive integer, and consider the Jacobi map Jn . (a) Show that .Zn /2 Ker Jn . (b) Show that if n is the square of an integer, then Ker Jn D Zn . (c) Show that if n is not the square of an integer, then ŒZn W Ker Jn D 2 and ŒKer Jn W .Zn /2 D 2r 1 , where r is the number of distinct prime divisors of n. E XERCISE 12.4. Let p and q be distinct primes, with p q 3 .mod 4/, and let n WD pq. (a) Show that Œ 1n 2 Ker Jn n .Zn /2 , and from this, conclude that the cosets of .Zn /2 in Ker Jn are the two distinct cosets .Zn /2 and Œ 1n .Zn /2 . (b) Let ı 2 Zn n Ker Jn . Show that the map from f0; 1g f0; 1g .Zn /2 to Zn that sends .a; b; / to ı a . 1/b is a bijection. 12.3 Computing the Jacobi symbol Suppose we are given an odd, positive integer n, along with an integer a, and we want to compute the Jacobi symbol .a j n/. Theorem 12.5 suggests the following algorithm: 1 repeat // loop invariant: n is odd and positive a a mod n if a D 0 then if n D 1 then return else return 0 compute a0 ; h such that a D 2h a0 and a0 is odd if h 6 0 .mod 2/ and n 6 ˙1 .mod 8/ then if a0 6 1 .mod 4/ and n 6 1 .mod 4/ then .a; n/ .n; a0 / forever
That this algorithm correctly computes the Jacobi symbol .a j n/ follows directly from Theorem 12.5. Using an analysis similar to that of Euclid’s algorithm, one easily sees that the running time of this algorithm is O.len.a/ len.n//. E XERCISE 12.5. Develop a “binary” Jacobi symbol algorithm, that is, one that uses only addition, subtractions, and “shift” operations, analogous to the binary gcd algorithm in Exercise 4.5.
12.4 Testing quadratic residuosity
347
E XERCISE 12.6. This exercise develops a probabilistic primality test based on the Jacobi symbol. For odd integer n > 1, define Gn WD f˛ 2 Zn W ˛ .n
1/=2
D Jn .˛/g;
where Jn W Zn ! f˙1g is the Jacobi map. (a) Show that Gn is a subgroup of Zn . (b) Show that if n is prime, then Gn D Zn . (c) Show that if n is composite, then Gn ¨ Zn . (d) Based on parts (a)–(c), design and analyze an efficient probabilistic primality test that works by choosing a random, non-zero element ˛ 2 Zn , and testing if ˛ 2 Gn . 12.4 Testing quadratic residuosity In this section, we consider the problem of testing whether a is a quadratic residue modulo n, for given integers a and n, from a computational perspective. 12.4.1 Prime modulus For an odd prime p, we can test if an integer a is a quadratic residue modulo p by either performing the exponentiation a.p 1/=2 mod p or by computing the Legendre symbol .a j p/. Assume that 0 a < p. Using a standard repeated squaring algorithm, the former method takes time O.len.p/3 /, while using the Euclideanlike algorithm of the previous section, the latter method takes time O.len.p/2 /. So clearly, the latter method is to be preferred. 12.4.2 Prime-power modulus For an odd prime p, we know that a is a quadratic residue modulo p e if and only if a is a quadratic residue modulo p (see Theorem 2.30). So this case immediately reduces to the previous case. 12.4.3 Composite modulus For odd, composite n, if we know the factorization of n, then we can also determine if a is a quadratic residue modulo n by determining if it is a quadratic residue modulo each prime divisor p of n (see Exercise 2.39). However, without knowledge of this factorization (which is in general believed to be hard to compute), there is no efficient algorithm known. We can compute the Jacobi symbol .a j n/; if this is
348
Quadratic reciprocity and computing modular square roots
1 or 0, we can conclude that a is not a quadratic residue; otherwise, we cannot conclude much of anything. 12.5 Computing modular square roots In this section, we consider the problem of computing a square root of a modulo n, given integers a and n, where a is a quadratic residue modulo n. 12.5.1 Prime modulus Let p be an odd prime, and let a be an integer such that 0 < a < p and .a j p/ D 1. We would like to compute a square root of a modulo p. Let ˛ WD Œap 2 Zp , so that we can restate our problem of that of finding ˇ 2 Zp such that ˇ 2 D ˛, given ˛ 2 .Zp /2 . We first consider the special case where p 3 .mod 4/, in which it turns out that this problem can be solved very easily. Indeed, we claim that in this case ˇ WD ˛ .pC1/=4 is a square root of ˛ — note that since p 3 .mod 4/, the number .p C 1/=4 is an integer. To show that ˇ 2 D ˛, suppose ˛ D ˇQ 2 for some ˇQ 2 Zp . We know that Q since we are assuming that ˛ 2 .Z /2 . Then we have there is such a ˇ, p ˇ 2 D ˛ .pC1/=2 D ˇQ pC1 D ˇQ 2 D ˛; where we used Fermat’s little theorem for the third equality. Using a repeatedsquaring algorithm, we can compute ˇ in time O.len.p/3 /. Now we consider the general case, where we may have p 6 3 .mod 4/. Here is one way to efficiently compute a square root of ˛, assuming we are given, in addition to ˛, an auxiliary input 2 Zp n .Zp /2 (how one obtains such a is discussed below). Let us write p 1 D 2h m, where m is odd. For every ı 2 Zp , ı m has multiplicah 1
tive order dividing 2h . Since ˛ 2 m D 1, ˛ m has multiplicative order dividing h 1 2h 1 . Since 2 m D 1, m has multiplicative order precisely 2h . Since there is only one subgroup of Zp of order 2h , it follows that m generates this subgroup, and that ˛ m D mx for 0 x < 2h and x is even. We can find x by computing the discrete logarithm of ˛ m to the base m , using the algorithm in §11.2.3. Setting D mx=2 , we have 2 D ˛m: We are not quite done, since we now have a square root of ˛ m , and not of ˛.
349
12.5 Computing modular square roots
Since m is odd, we may write m D 2t C 1 for some non-negative integer t . It then follows that .˛ t /2 D 2 ˛
2t
D ˛m˛
2t
D ˛m
2t
D ˛:
Thus, ˛ t is a square root of ˛. Let us summarize the above algorithm for computing a square root of ˛ 2 .Zp /2 , assuming we are given 2 Zp n .Zp /2 , in addition to ˛: compute positive integers m; h such that p 1 D 2h m with m odd
0
m, ˛0 ˛m compute x log 0 ˛ 0 // note that 0 x < 2h and x is even ˇ . 0 /x=2 ˛ bm=2c output ˇ The total amount of work done outside the discrete logarithm calculation amounts to just a handful of exponentiations modulo p, and so takes time O.len.p/3 /. The time to compute the discrete logarithm is O.h len.h/ len.p/2 /. So the total running time of this procedure is O.len.p/3 C h len.h/ len.p/2 /: The above procedure assumed we had at hand a non-square . If h D 1, which means that p 3 .mod 4/, then . 1 j p/ D 1, and so we are done. However, we have already seen how to efficiently compute a square root in this case. If h > 1, we can find a non-square using a probabilistic search algorithm. Simply choose at random, test if it is a square, and if so, repeat. The probability that a random element of Zp is a square is 1=2; thus, the expected number of trials until we find a non-square is 2, and hence the expected running time of this probabilistic search algorithm is O.len.p/2 /. 12.5.2 Prime-power modulus Let p be an odd prime, let a be an integer relatively prime to p, and let e > 1 be an integer. We know that a is a quadratic residue modulo p e if and only if a is a quadratic residue modulo p. Suppose that a is a quadratic residue modulo p, and that we have found an integer b such that b 2 a .mod p/, using, say, one of the procedures described in §12.5.1. From this, we can easily compute a square root of a modulo p e using the following technique, which is known as Hensel lifting. More generally, suppose we have computed an integer b such that b 2 a .mod p f /, for some f 1, and we want to find an integer c such that
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c 2 a .mod p f C1 /. Clearly, if c 2 a .mod p f C1 /, then c 2 a .mod p f /, and so c ˙b .mod p f /. So let us set c D b C p f h, and solve for h. We have c 2 .b C p f h/2 b 2 C 2bp f h C p 2f h2 b 2 C 2bp f h .mod p f C1 /: So we want to find an integer h satisfying 2bp f h a Since p f j .b 2
b 2 .mod p f C1 /:
a/, by Theorem 2.5, the above congruence holds if and only if 2bh
a
b2 pf
.mod p/:
From this, we can easily compute the desired value h, since gcd.2b; p/ D 1. By iterating the above procedure, starting with a square root of a modulo p, we can quickly find a square root of a modulo p e . We leave a detailed analysis of the running time of this procedure to the reader. 12.5.3 Composite modulus To find square roots modulo n, where n is an odd composite modulus, if we know the prime factorization of n, then we can use the above procedures for finding square roots modulo primes and prime powers, and then use the algorithm of the Chinese remainder theorem to get a square root modulo n. However, if the factorization of n is not known, then there is no efficient algorithm known for computing square roots modulo n. In fact, one can show that the problem of finding square roots modulo n is at least as hard as the problem of factoring n, in the sense that if there is an efficient algorithm for computing square roots modulo n, then there is an efficient (probabilistic) algorithm for factoring n. We now present an algorithm to factor n, using a modular square-root algorithm A as a subroutine. For simplicity, we assume that A is deterministic, and that for all n and for all ˛ 2 .Zn /2 , A.n; ˛/ outputs a square root of ˛. Also for simplicity, we shall assume that n is of the form n D pq, where p and q are distinct, odd primes. In Exercise 12.16 below, you are asked to relax these restrictions. Our algorithm runs as follows:
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12.5 Computing modular square roots
ˇ R ZC gcd.rep.ˇ/; n/ n,d if d > 1 then output d else ˛ ˇ2, ˇ0 A.n; ˛/ 0 if ˇ D ˙ˇ then output “failure” else output gcd.rep.ˇ
ˇ 0 /; n/
Here, ZC n denotes the set of non-zero elements of Zn . Also, recall that rep.ˇ/ denotes the canonical representative of ˇ. First, we argue that the algorithm outputs either “failure” or a non-trivial factor of n. Clearly, if ˇ … Zn , then the value d computed by the algorithm is a nontrivial factor. So suppose ˇ 2 Zn . In this case, the algorithm invokes A on inputs n and ˛ WD ˇ 2 , obtaining a square root ˇ 0 of ˛. Suppose that ˇ ¤ ˙ˇ 0 , and set
WD ˇ ˇ 0 . What we need to show is that gcd.rep. /; n/ is a non-trivial factor of n. To see this, consider the ring isomorphism of the Chinese remainder theorem W
Zn ! Zp Zq Œan 7! .Œap ; Œaq /:
Suppose .ˇ 0 / D .ˇ10 ; ˇ20 /. Then the four square roots of ˛ are ˇ0 D
1
.ˇ10 ; ˇ20 /;
ˇ0 D
1
. ˇ10 ; ˇ20 /;
1
. ˇ10 ; ˇ20 /;
1
.ˇ10 ; ˇ20 /:
The assumption that ˇ ¤ ˙ˇ 0 implies that .ˇ/ D . ˇ10 ; ˇ20 / or .ˇ/ D .ˇ10 ; ˇ20 /. In the first case, . / D . 2ˇ10 ; 0/, which implies gcd.rep. /; n/ D q. In the second case, . / D .0; 2ˇ20 /, which implies gcd.rep. /; n/ D p. Second, we argue that PŒF 1=2, where F is the event that the algorithm outputs “failure.” Viewed as a random variable, ˇ is uniformly distributed over 0 2 ZC n . Clearly, PŒF j ˇ … Zn D 0. Now consider any fixed ˛ 2 .Zn / . Observe 2 0 that the conditional distribution of ˇ given that ˇ D ˛ is essentially the uniform distribution on the set of four square roots of ˛ 0 . Also observe that the output of A depends only on n and ˇ 2 , and so with respect to the conditional distribution given that ˇ 2 D ˛ 0 , the output ˇ 0 of A is fixed. Thus, PŒF j ˇ 2 D ˛ 0 D PŒˇ D ˙ˇ 0 j ˇ 2 D ˛ 0 D 1=2:
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Quadratic reciprocity and computing modular square roots
Putting everything together, using total probability, we have X PŒF D PŒF j ˇ … Z PŒF j ˇ 2 D ˛ 0 PŒˇ 2 D ˛ 0 n PŒˇ … Zn C 2 ˛ 0 2.Z n/
X
D 0 PŒˇ … Zn C
2 ˛ 0 2.Z n/
1 1 PŒˇ 2 D ˛ 0 : 2 2
Thus, the above algorithm fails to split n with probability at most 1=2. If we like, we can repeat the algorithm until it succeeds. The expected number of iterations performed will be at most 2. E XERCISE 12.7. Let p be an odd prime, and let f 2 Zp ŒX be a polynomial with 0 deg.f / 2. Design and analyze an efficient, deterministic algorithm that takes as input p, f , and an element of Zp n .Zp /2 , and which determines if f has any roots in Zp , and if so, finds all of the roots. Hint: see Exercise 7.16. E XERCISE 12.8. Show how to deterministically compute square roots modulo primes p 5 .mod 8/ in time O.len.p/3 /. E XERCISE 12.9. This exercise develops an alternative algorithm for computing square roots modulo a prime. Let p be an odd prime, let ˇ 2 Zp , and set ˛ WD ˇ 2 . Define B˛ WD f 2 Zp W 2 ˛ 2 .Zp /2 g. (a) Show that B˛ D f 2 Zp W g. / D 0g, where g WD .X
ˇ/.p
1/=2
.X C ˇ/.p
1/=2
2 Zp ŒX:
(b) Let 2 Zp nB˛ , and suppose 2 ¤ ˛. Let ; be the uniquely determined elements of Zp satisfying the polynomial congruence C X . Show that D 0 and
2
X/.p
1/=2
.mod X2
˛/:
D ˛.
(c) Using parts (a) and (b), design and analyze a probabilistic algorithm that computes a square root of a given ˛ 2 .Zp /2 in expected time O.len.p/3 /. Note that when p 1 D 2h m (m odd), and h is large (e.g., h len.p/=2), the algorithm in the previous exercise is asymptotically faster than the one in §12.5.1; however, the latter algorithm is likely to be faster in practice for the typical case where h is small. E XERCISE 12.10. Let p be a prime, and consider the natural map that sends a 2 Z to aN WD Œap 2 Zp , which we may extend coeffecient-wise to a ring homomorphism from ZŒX to Zp ŒX, as in Example 7.47. Let f 2 ZŒX and
12.5 Computing modular square roots
353
x 2 Z, such that xN 2 Zp is a simple root of fN 2 Zp ŒX (see definitions after Exercise 7.17). Show that for every positive integer e, there exists an integer y such that f .y/ 0 .mod p e /, and give an efficient procedure to compute such a y, given p, f , x, and e. E XERCISE 12.11. Show that the following two problems are deterministic, polytime equivalent (see discussion just above Exercise 11.10 in §11.3): (a) Given an odd prime p and ˛ 2 .Zp /2 , find ˇ 2 Zp such that ˇ 2 D ˛. (b) Given an odd prime p, find an element of Zp n .Zp /2 . E XERCISE 12.12. Design and analyze an efficient, deterministic algorithm that takes as input primes p and q, such that q j .p 1/, along with an element ˛ 2 Zp , and determines whether or not ˛ 2 .Zp /q . E XERCISE 12.13. Design and analyze an efficient, deterministic algorithm that takes as input primes p and q, such that q j .p 1/ but q 2 − .p 1/, along with an element ˛ 2 .Zp /q , and computes a qth root of ˛, that is, an element ˇ 2 Zp such that ˇ q D ˛. E XERCISE 12.14. Design and analyze an algorithm that takes as input primes p and q, such that q j .p 1/, along with an element ˛ 2 .Zp /q , and computes a qth root of ˛. (Unlike Exercise 12.13, we now allow q 2 j .p 1/.) Your algorithm may be probabilistic, and should have an expected running time that is bounded by q 1=2 times a polynomial in len.p/. Hint: Exercise 4.10 may be useful. E XERCISE 12.15. Let p be an odd prime, be a generator for Zp , and ˛ be any element of Zp . Define 1 if log ˛ .p 1/=2; B.p; ; ˛/ WD 0 if log ˛ < .p 1/=2. Suppose that there is an algorithm that efficiently computes B.p; ; ˛/ for all p; ; ˛ as above. Show how to use this algorithm as a subroutine in an efficient, probabilistic algorithm that computes log ˛ for all p; ; ˛ as above. Hint: in addition to the algorithm that computes B, use algorithms for testing quadratic residuosity and computing square roots modulo p, and “read off” the bits of log ˛ one at a time. E XERCISE 12.16. Suppose there is a probabilistic algorithm A that takes as input a positive integer n, and an element ˛ 2 .Zn /2 . Assume that for all n, and for a randomly chosen ˛ 2 .Zn /2 , A computes a square root of ˛ with probability at least 0:001. Here, the probability is taken over the random choice of ˛ and the random choices of A. Show how to use A to construct another probabilistic
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Quadratic reciprocity and computing modular square roots
algorithm A0 that takes n as input, runs in expected polynomial time, and that satisfies the following property: for all n, A0 outputs the complete factorization of n into primes with probability at least 0:999. E XERCISE 12.17. Suppose there is a probabilistic algorithm A that takes as input positive integers n and m, and an element ˛ 2 .Zn /m . It outputs either “failure,” or an mth root of ˛. Furthermore, assume that A runs in expected polynomial time, and that for all n and m, and for randomly chosen ˛ 2 .Zn /m , A succeeds in computing an mth root of ˛ with probability .n; m/. Here, the probability is taken over the random choice of ˛, as well as the random choices made during the execution of A. Show how to use A to construct another probabilistic algorithm A0 that takes as input n, m, and ˛ 2 .Zn /m , runs in expected polynomial time, and that satisfies the following property: if .n; m/ 0:001, then for all ˛ 2 .Zn /m , A0 computes an mth root of ˛ with probability at least 0:999. 12.6 The quadratic residuosity assumption Loosely speaking, the quadratic residuosity (QR) assumption is the assumption that it is hard to distinguish squares from non-squares in Zn , where n is of the form n D pq, and p and q are distinct primes. This assumption plays an important role in cryptography. Of course, since the Jacobi symbol is easy to compute, for this assumption to make sense, we have to restrict our attention to elements of Ker Jn , where Jn W Zn ! f˙1g is the Jacobi map. We know that .Zn /2 Ker Jn (see Exercise 12.3). Somewhat more precisely, the QR assumption is the assumption that it is hard to distinguish a random element in Ker Jn n .Zn /2 from a random element in .Zn /2 , given n (but not its factorization!). To give a rough idea as to how this assumption may be used in cryptography, assume that p q 3 .mod 4/, so that Œ 1n 2 Ker Jn n .Zn /2 , and moreover, Ker Jn n .Zn /2 D Œ 1n .Zn /2 (see Exercise 12.4). The value n can be used as a public key in a public-key cryptosystem (see §4.7). Alice, knowing the public key, can encrypt a single bit b 2 f0; 1g as ˇ WD . 1/b ˛ 2 , where Alice chooses ˛ 2 Zn at random. The point is, if b D 0, then ˇ is uniformly distributed over .Zn /2 , and if b D 1, then ˇ is uniformly distributed over Ker Jn n .Zn /2 . Now Bob, knowing the secret key, which is the factorization of n, can easily determine if ˇ 2 .Zn /2 or not, and hence deduce the value of the encrypted bit b. However, under the QR assumption, an eavesdropper, seeing just n and ˇ, cannot effectively figure out what b is. Of course, the above scheme is much less efficient than the RSA cryptosystem
12.7 Notes
355
presented in §4.7, but nevertheless, has attractive properties; in particular, its security is very closely tied to the QR assumption, whereas the security of RSA is a bit less well understood. E XERCISE 12.18. Suppose that A is a probabilistic algorithm that takes as input n of the form n D pq, where p and q are distinct primes such that p q 3 .mod 4/. The algorithm also takes as input ˛ 2 Ker Jn , and outputs either 0 or 1. Furthermore, assume that A runs in expected polynomial time. Define two random variables, Xn and Yn , as follows: Xn is defined to be the output of A on input n and a value ˛ chosen at random from Ker Jn n .Zn /2 , and Yn is defined to be the output of A on input n and a value ˛ chosen at random from .Zn /2 . In both cases, the value of the random variable is determined by the random choice of ˛, as well as the random choices made by the algorithm. Define .n/ WD jPŒXn D 1 PŒYn D 1j. Show how to use A to design a probabilistic, expected polynomial time algorithm A0 that takes as input n as above and ˛ 2 Ker Jn , and outputs either “square” or “non-square,” with the following property: if .n/ 0:001, then for all ˛ 2 Ker Jn , the probability that A0 correctly identifies whether ˛ 2 .Zn /2 is at least 0:999. Hint: use the Chernoff bound. E XERCISE 12.19. Assume the same notation as in the previous exercise. Define the random variable Xn0 to be the output of A on input n and a value ˛ chosen at random from Ker Jn . Show that jPŒXn0 D 1 PŒYn D 1j D .n/=2. Thus, the problem of distinguishing Ker Jn from .Zn /2 is essentially equivalent to the problem of distinguishing Ker Jn n .Zn /2 from .Zn /2 . 12.7 Notes The proof we present here of Theorem 12.1 is essentially the one from Niven and Zuckerman [70]. Our proof of Theorem 12.5 is essentially the one found in Bach and Shallit [11]. Exercise 12.6 is based on Solovay and Strassen [97]. The probabilistic algorithm in §12.5.1 can be made deterministic under a generalization of the Riemann hypothesis. Indeed, as discussed in §10.5, under such a hypothesis, Bach’s result [10] implies that the least positive integer that is not a quadratic residue modulo p is at most 2 log p (this follows by applying Bach’s result with the subgroup .Zp /2 of Zp ). Thus, we may find the required element
2 Zp n .Zn /2 in deterministic polynomial time, just by brute-force search. The best unconditional bound on the smallest positive integer that is not a quadratic
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Quadratic reciprocity and computing modular square roots
residue modulo p is due to Burgess [22], who gives a bound of p ˛Co.1/ , where p ˛ WD 1=.4 e/ 0:15163. Goldwasser and Micali [40] introduced the quadratic residuosity assumption to cryptography (as discussed in §12.6). This assumption has subsequently been used as the basis for numerous cryptographic schemes.
13 Modules and vector spaces
In this chapter, we introduce the basic definitions and results concerning modules over a ring R and vector spaces over a field F . The reader may have seen some of these notions before, but perhaps only in the context of vector spaces over a specific field, such as the real or complex numbers, and not in the context of, say, finite fields like Zp . 13.1 Definitions, basic properties, and examples Throughout this section, R denotes a ring (i.e., a commutative ring with unity). Definition 13.1. An R-module is a set M together with an addition operation on M and a function W R M ! M , such that the set M under addition forms an abelian group, and moreover, for all c; d 2 R and ˛; ˇ 2 M , we have: (i) .c; .d; ˛// D .cd; ˛/, (ii) .c C d; ˛/ D .c; ˛/ C .d; ˛/, (iii) .c; ˛ C ˇ/ D .c; ˛/ C .c; ˇ/, (iv) .1R ; ˛/ D ˛. One may also call an R-module M a module over R, and elements of R are sometimes called scalars. The function in the definition is called a scalar multiplication map, and usually, when the context is clear, we shall write c˛ instead of .c; ˛/. When we do this, properties (i)–(iv) of the definition may be written as follows: c.d˛/ D .cd /˛; .c C d /˛ D c˛ C d˛; c.˛ C ˇ/ D c˛ C cˇ; 1R ˛ D ˛: Note that by property (i), we may write cd˛ without any ambiguity, as both possible interpretations, c.d˛/ and .cd /˛, yield the same value.
357
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Modules and vector spaces
For fixed c 2 R, the map that sends ˛ 2 M to c˛ 2 M is a group homomorphism with respect to the additive group operation of M ; likewise, for fixed ˛ 2 M , the map that sends c 2 R to c˛ 2 M is a group homomorphism from the additive group of R into the additive group of M . Combining these observations with basic facts about group homomorphisms (see Theorem 6.19), we may easily derive the following basic facts about R-modules: Theorem 13.2. If M is a module over R, then for all c 2 R, ˛ 2 M , and k 2 Z, we have: (i) 0R ˛ D 0M , (ii) c 0M D 0M , (iii) . c/˛ D .c˛/ D c. ˛/, (iv) .kc/˛ D k.c˛/ D c.k˛/. Proof. Exercise. The definition of a module includes the trivial module, consisting of just the zero element 0M . If R is the trivial ring, then any R-module is trivial, since for every ˛ 2 M , we have ˛ D 1R ˛ D 0R ˛ D 0M . Example 13.1. The ring R itself can be viewed as an R-module in the obvious way, with addition and scalar multiplication defined in terms of the addition and multiplication operations of R. Example 13.2. The set Rn , which consists of all of n-tuples of elements of R, forms an R-module, where addition and scalar multiplication are defined component-wise; that is, for ˛ D .a1 ; : : : ; an / 2 Rn , ˇ D .b1 ; : : : ; bn / 2 Rn , and c 2 R, we have ˛ C ˇ WD .a1 C b1 ; : : : ; an C bn / and c˛ WD .ca1 ; : : : ; can /: Example 13.3. The ring of polynomials RŒX over R forms an R-module in the natural way, with addition and scalar multiplication defined in terms of the addition and multiplication operations of the polynomial ring. Example 13.4. As in Example 7.39, let f be a non-zero polynomial over R with lc.f / 2 R , and consider the quotient ring E WD RŒX=.f /. Then E is a module over R, with addition defined in terms of the addition operation of E, and scalar multiplication defined by cŒgf WD Œcf Œgf D Œcgf , for c 2 R and g 2 RŒX. Example 13.5. Generalizing Example 13.3, if E is any ring containing R as a subring (i.e., E is an extension ring of R), then E is a module over R, with addition and scalar multiplication defined in terms of the addition and multiplication operations of E.
13.2 Submodules and quotient modules
359
Example 13.6. Any abelian group G, written additively, can be viewed as a Zmodule, with scalar multiplication defined in terms of the usual integer multiplication map (see Theorem 6.4). Example 13.7. Let G be any group, written additively, whose exponent divides n. Then we may define a scalar multiplication that maps Œkn 2 Zn and ˛ 2 G to k˛. That this map is unambiguously defined follows from the fact that G has exponent dividing n, so that if k k 0 .mod n/, we have k˛ k 0 ˛ D .k k 0 /˛ D 0G , since n j .k k 0 /. It is easy to check that this scalar multiplication map indeed makes G into a Zn -module. Example 13.8. Of course, viewing a group as a module does not depend on whether or not we happen to use additive notation for the group operation. If we specialize the previous example to the group G D Zp , where p is prime, then we may view G as a Zp 1 -module. However, since the group operation itself is written multiplicatively, the “scalar product” of Œkp 1 2 Zp 1 and ˛ 2 Zp is the power ˛ k . Example 13.9. If M1 ; : : : ; Mk are R-modules, then so is their direct product M1 Mk , where addition and scalar product are defined component-wise. If M D M1 D D Mk , we write this as M k . Example 13.10. If I is an arbitrary set, and M is an R-module, then Map.I; M /, which is the set of all functions f W I ! M , may be naturally viewed as an Rmodule, with point-wise addition and scalar multiplication: for f; g 2 Map.I; M / and c 2 R, we define .f C g/.i/ WD f .i / C g.i / and .cf /.i / WD cf .i / for all i 2 I : 13.2 Submodules and quotient modules Again, throughout this section, R denotes a ring. The notions of subgroups and quotient groups extend in the obvious way to R-modules. Definition 13.3. Let M be an R-module. A subset N of M is a submodule (over R) of M if (i) N is a subgroup of the additive group M , and (ii) N is closed under scalar multiplication; that is, for all c 2 R and ˛ 2 N , we have c˛ 2 N . It is easy to see that a submodule N of an R-module M is also an R-module in its own right, with addition and scalar multiplication operations inherited from M .
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Modules and vector spaces
Expanding the above definition, we see that a non-empty subset N of M is a submodule if and only if for all c 2 R and all ˛; ˇ 2 N , we have ˛ C ˇ 2 N;
˛ 2 N; and c˛ 2 N:
Observe that the condition ˛ 2 N is redundant, as it is implied by the condition c˛ 2 N with c D 1R . Clearly, f0M g and M are submodules of M . For k 2 Z, it is easy to see that not only are kM and M fkg subgroups of M (see Theorems 6.7 and 6.8), they are also submodules of M . Moreover, for c 2 R, cM WD fc˛ W ˛ 2 M g and M fcg WD f˛ 2 M W c˛ D 0M g are also submodules of M . Further, for ˛ 2 M , R˛ WD fc˛ W c 2 Rg is a submodule of M . Finally, if N1 and N2 are submodules of M , then N1 C N2 and N1 \ N2 are not only subgroups of M , they are also submodules of M . We leave it to the reader to verify all these facts: they are quite straightforward. Let ˛1 ; : : : ; ˛k 2 M . The submodule R˛1 C C R˛k is called the submodule (over R) generated by ˛1 ; : : : ; ˛k . It consists of all Rlinear combinations c1 ˛1 C C ck ˛k ; where the ci ’s are elements of R, and is the smallest submodule of M that contains the elements ˛1 ; : : : ; ˛k . We shall also write this submodule as h˛1 ; : : : ; ˛k iR . As a matter of definition, we allow k D 0, in which case this submodule is f0M g. We say that M is finitely generated (over R) if M D h˛1 ; : : : ; ˛k iR for some ˛1 ; : : : ; ˛k 2 M . Example 13.11. For a given integer ` 0, define RŒX<` to be the set of polynomials of degree less than `. The reader may verify that RŒX<` is a submodule of the R-module RŒX, and indeed, is the submodule generated by 1; X; : : : ; X` 1 . If ` D 0, then this submodule is the trivial submodule f0R g. Example 13.12. Let G be an abelian group. As in Example 13.6, we can view G as a Z-module in a natural way. Subgroups of G are just the same thing as submodules of G, and for a1 ; : : : ; ak 2 G, the subgroup ha1 ; : : : ; ak i is the same as the submodule ha1 ; : : : ; ak iZ .
13.3 Module homomorphisms and isomorphisms
361
Example 13.13. As in Example 13.1, we may view the ring R itself as an Rmodule. With respect to this module structure, ideals of R are just the same thing as submodules of R, and for a1 ; : : : ; ak 2 R, the ideal .a1 ; : : : ; ak / is the same as the submodule ha1 ; : : : ; ak iR . Note that for a 2 R, the ideal generated by a may be written either as aR, using the notation introduced in §7.3, or as Ra, using the notation introduced in this section. Example 13.14. If E is an extension ring of R, then we may view E as an Rmodule, as in Example 13.5. It is easy to see that every ideal of E is a submodule; however, the converse is not true in general. Indeed, the submodule RŒX<` of RŒX discussed in Example 13.11 is not an ideal of the ring RŒX. If N is a submodule of M , then in particular, it is also a subgroup of M , and we can form the quotient group M=N in the usual way (see §6.3), which consists of all cosets Œ˛N , where ˛ 2 M . Moreover, because N is closed under scalar multiplication, we can also define a scalar multiplication on M=N in a natural way. Namely, for c 2 R and ˛ 2 M , we define c Œ˛N WD Œc˛N : As usual, one must check that this definition is unambiguous, that is, if ˛ ˛ 0 .mod N /, then c˛ c˛ 0 .mod N /. But this follows from the fact that N is closed under scalar multiplication (verify). One can also easily check (verify) that with scalar multiplication defined in this way, M=N is an R-module; it is called the quotient module (over R) of M modulo N . Example 13.15. Suppose E is an extension ring of R, and I is an ideal of E. Viewing E as an R-module, I is a submodule of E, and hence the quotient ring E=I may naturally be viewed as an R-module, with scalar multiplication defined by c Œ˛I WD Œc˛I for c 2 R and ˛ 2 E. Example 13.4 is a special case of this, applied to the extension ring RŒX and the ideal .f /. E XERCISE 13.1. Let M1 and M2 be R-modules, and let N1 be a submodule of M1 and N2 a submodule of M2 . Show that N1 N2 is a submodule of M1 M2 . E XERCISE 13.2. Show that if R is non-trivial, then RŒX is not finitely generated. 13.3 Module homomorphisms and isomorphisms Again, throughout this section, R is a ring. The notion of a group homomorphism extends in the obvious way to R-modules.
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Modules and vector spaces
Definition 13.4. Let M and M 0 be modules over R. An R-module homomorphism from M to M 0 is a function W M ! M 0 , such that (i) is a group homomorphism from M to M 0 , and (ii) for all c 2 R and ˛ 2 M , we have .c˛/ D c.˛/. An R-module homomorphism is also called an R-linear map. We shall use this terminology from now on. Expanding the definition, we see that a map W M ! M 0 is an R-linear map if and only if .˛ C ˇ/ D .˛/ C .ˇ/ and .c˛/ D c.˛/ for all ˛; ˇ 2 M and all c 2 R. Example 13.16. If N is a submodule of an R-module M , then the inclusion map i W N ! M is obviously an R-linear map. Example 13.17. Suppose N is a submodule of an R-module M . Then the natural map (see Example 6.36) W M ! M=N ˛ 7! Œ˛N is not just a group homomorphism, it is also easily seen to be an R-linear map. Example 13.18. Let M be an R-module, and let k be an integer. Then the kmultiplication map on M (see Example 6.38) is not only a group homomorphism, but it is also easily seen to be an R-linear map. Its image is the submodule kM , and its kernel the submodule M fkg. Example 13.19. Let M be an R-module, and let c be an element of R. The map W M !M ˛ 7! c˛ is called c-multiplication map on M , and is easily seen to be an R-linear map whose image is the submodule cM , and whose kernel is the submodule M fcg. The set of all c 2 R for which cM D f0M g is called the R-exponent of M , and is easily seen to be an ideal of R. Example 13.20. Let M be an R-module, and let ˛ be an element of M . The map W R!M c 7! c˛ is easily seen to be an R-linear map whose image is the submodule R˛ (i.e., the submodule generated by ˛). The kernel of this map is called the R-order of ˛, and is easily seen to be an ideal of R.
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Example 13.21. Generalizing the previous example, let M be an R-module, and let ˛1 ; : : : ; ˛k be elements of M . The map W
Rk ! M .c1 ; : : : ; ck / 7! c1 ˛1 C C ck ˛k
is easily seen to be an R-linear map whose image is the submodule R˛1 C CR˛k (i.e., the submodule generated by ˛1 ; : : : ; ˛k ). Example 13.22. Suppose that M1 ; : : : ; Mk are submodules of an R-module M . Then the map W M1 Mk ! M .˛1 ; : : : ; ˛k / 7! ˛1 C C ˛k is easily seen to be an R-linear map whose image is the submodule M1 C CMk . Example 13.23. Let E be an extension ring of R. As we saw in Example 13.5, E may be viewed as an R-module in a natural way. Let ˛ 2 E, and consider the ˛-multiplication map on E, which sends ˇ 2 E to ˛ˇ 2 E. Then it is easy to see that this is an R-linear map. Example 13.24. Let E and E 0 be extension rings of R, which may be viewed as R-modules as in Example 13.5. Suppose that W E ! E 0 is a ring homomorphism whose restriction to R is the identity map (i.e., .c/ D c for all c 2 R). Then is an R-linear map. Indeed, for every c 2 R and ˛; ˇ 2 E, we have .˛ C ˇ/ D .˛/ C .ˇ/ and .c˛/ D .c/.˛/ D c.˛/. Example 13.25. Let G and G 0 be abelian groups. As we saw in Example 13.6, G and G 0 may be viewed as Z-modules. In addition, every group homomorphism W G ! G 0 is also a Z-linear map. Since an R-module homomorphism is also a group homomorphism on the underlying additive groups, all of the statements in Theorem 6.19 apply. In particular, an R-linear map is injective if and only if the kernel is trivial (i.e., contains only the zero element). However, in the case of R-module homomorphisms, we can extend Theorem 6.19, as follows: Theorem 13.5. Let W M ! M 0 be an R-linear map. (i) For every submodule N of M , .N / is a submodule of M 0 ; in particular (setting N WD M ), Im is a submodule of M 0 . (ii) For every submodule N 0 of M 0 , 1 .N 0 / is a submodule of M 0 ; in particular (setting N 0 WD f0M 0 g), Ker is a submodule of M .
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Proof. Exercise. Theorems 6.20 and 6.21 have natural R-module analogs, which the reader may easily verify: Theorem 13.6. If W M ! M 0 and 0 W M 0 ! M 00 are R-linear maps, then so is their composition 0 B W M ! M 00 . Theorem 13.7. Let i W M ! Mi0 , for i D 1; : : : ; k, be R-linear maps. Then the map W M ! M10 Mk0 ˛ 7! .1 .˛/; : : : ; k .˛// is an R-linear map. If an R-linear map W M ! M 0 is bijective, then it is called an R-module isomorphism of M with M 0 . If such an R-module isomorphism exists, we say that M is isomorphic to M 0 , and write M Š M 0 . Moreover, if M D M 0 , then is called an R-module automorphism on M . Theorems 6.22–6.26 also have natural R-module analogs, which the reader may easily verify: Theorem 13.8. If is a R-module isomorphism of M with M 0 , then the inverse function 1 is an R-module isomorphism of M 0 with M . Theorem 13.9 (First isomorphism theorem). Let W M ! M 0 be an R-linear map with kernel K and image N 0 . Then we have an R-module isomorphism M=K Š N 0 : Specifically, the map N W M=K ! M 0 Œ˛K 7! .˛/ is an injective R-linear map whose image is N 0 . Theorem 13.10. Let W M ! M 0 be an R-linear map. Then for every submodule N of M with N Ker , we may define an R-linear map N W M=N ! M 0 Œ˛N 7! .˛/: Moreover, Im N D Im , and N is injective if and only if N D Ker .
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Theorem 13.11 (Internal direct product). Let M be an R-module with submodules N1 ; N2 , where N1 \ N2 D f0M g. Then we have an R-module isomorphism N1 N2 Š N1 C N2 given by the map W N1 N2 ! N1 C N2 .˛1 ; ˛2 / 7! ˛1 C ˛2 : Theorem 13.12. Let M and M 0 be R-modules, and consider the R-module of functions Map.M; M 0 /. Then HomR .M; M 0 / WD f 2 Map.M; M 0 / W is an R-linear mapg is a submodule of Map.M; M 0 /. Example 13.26. Consider again the R-module RŒX=.f / discussed in Example 13.4, where f 2 RŒX is of degree ` 0 and lc.f / 2 R . As an R-module, RŒX=.f / is isomorphic to RŒX<` (see Example 13.11). Indeed, based on the observations in Example 7.39, the map W RŒX<` ! RŒX=.f / that sends a polynomial g 2 RŒX of degree less than ` to Œgf 2 RŒX=.f / is an isomorphism of RŒX<` with RŒX=.f /. Furthermore, RŒX<` is isomorphic as an R-module to P R` . Indeed, the map 0 W RŒX<` ! R` that sends g D `i D01 ai Xi 2 RŒX<` to .a0 ; : : : ; a` 1 / 2 R` is an isomorphism of RŒX<` with R` . E XERCISE 13.3. Verify that the “is isomorphic to” relation on R-modules is an equivalence relation; that is, for all R-modules M1 ; M2 ; M3 , we have: (a) M1 Š M1 ; (b) M1 Š M2 implies M2 Š M1 ; (c) M1 Š M2 and M2 Š M3 implies M1 Š M3 . E XERCISE 13.4. Let i W Mi ! Mi0 , for i D 1; : : : ; k, be R-linear maps. Show that the map W M1 Mk ! M10 Mk0 .˛1 ; : : : ; ˛k / 7! .1 .˛1 /; : : : ; k .˛k // is an R-linear map. E XERCISE 13.5. Let W M ! M 0 be an R-linear map, and let c 2 R. Show that .cM / D c.M /. E XERCISE 13.6. Let W M ! M 0 be an R-linear map. Let N be a submodule of
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M , and let W N ! M 0 be the restriction of to N . Show that is an R-linear map and that Ker D Ker \ N . E XERCISE 13.7. Suppose M1 ; : : : ; Mk are R-modules. Show that for each i D 1; : : : ; k, the projection map i W M1 Mk ! Mi that sends .˛1 ; : : : ; ˛k / to ˛i is a surjective R-linear map. E XERCISE 13.8. Show that if M D M1 M2 for R-modules M1 and M2 , and N1 is a subgroup of M1 and N2 is a subgroup of N2 , then we have an R-module isomorphism M=.N1 N2 / Š M1 =N1 M2 =N2 . E XERCISE 13.9. Let M be an R-module with submodules N1 and N2 . Show that we have an R-module isomorphism .N1 C N2 /=N2 Š N1 =.N1 \ N2 /. E XERCISE 13.10. Let M be an R-module with submodules N1 , N2 , and A, where N2 N1 . Show that .N1 \ A/=.N2 \ A/ is isomorphic to a submodule of N1 =N2 . E XERCISE 13.11. Let W M ! M 0 be an R-linear map with kernel K. Let N be a submodule of M . Show that we have an R-module isomorphism M=.N C K/ Š .M /=.N /. E XERCISE 13.12. Let W M ! M 0 be a surjective R-linear map. Let S be the set of all submodules of M that contain Ker , and let S 0 be the set of all submodules of M 0 . Show that the sets S and S 0 are in one-to-one correspondence, via the map that sends N 2 S to .N / 2 S 0 . 13.4 Linear independence and bases Throughout this section, R denotes a ring. Definition 13.13. Let M be an R-module, and let f˛i gniD1 be a family of elements of M . We say that f˛i gniD1 (i) is linearly dependent (over R) if there exist c1 ; : : : ; cn 2 R, not all zero, such that c1 ˛1 C C cn ˛n D 0M ; (ii) is linearly independent (over R) if it is not linearly dependent; (iii) spans M (over R) if for every ˛ 2 M , there exist c1 ; : : : ; cn 2 R such that c1 ˛1 C C cn ˛n D ˛; (iv) is a basis for M (over R) if it is linearly independent and spans M . The family f˛i gniD1 always spans some submodule of M , namely, the submodule N generated by ˛1 ; : : : ; ˛n . In this case, we may also call N the submodule (over R) spanned by f˛i gniD1 .
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13.4 Linear independence and bases
The family f˛i gniD1 may contain duplicates, in which case it is linearly dependent (unless R is trivial). Indeed, if, say, ˛1 D ˛2 , then setting c1 WD 1, c2 WD 1, P and c3 WD WD cn WD 0, we have the linear relation niD1 ci ˛i D 0M . If the family f˛i gniD1 contains 0M , then it is also linear dependent (unless R is trivial). Indeed, if, say, ˛1 D 0M , then setting c1 WD 1 and c2 WD WD cn WD 0, P we have the we have the linear relation niD1 ci ˛i D 0M . The family f˛i gniD1 may also be empty (i.e., n D 0), in which case it is linearly independent, and spans the submodule f0M g. In the above definition, the ordering of the elements ˛1 ; : : : ; ˛n makes no difference. As such, when convenient, we may apply the terminology in the definition to any family f˛i gi 2I , where I is an arbitrary, finite index set. Example 13.27. Consider the R-module Rn . Define ˛1 ; : : : ; ˛n 2 Rn as follows: ˛1 WD .1; 0; : : : ; 0/; ˛2 WD .0; 1; 0; : : : ; 0/; : : : ; ˛n WD .0; : : : ; 0; 1/I that is, ˛i has a 1 in position i and is zero everywhere else. It is easy to see that f˛i gniD1 is a basis for Rn . Indeed, for every c1 ; : : : ; cn 2 R, we have c1 ˛1 C C cn ˛n D .c1 ; : : : ; cn /, from which it is clear that f˛i gniD1 spans Rn and is linearly independent. The family f˛i gniD1 is called the standard basis for Rn . Example 13.28. Consider the Z-module Z3 . In addition to the standard basis, which consists of the tuples .1; 0; 0/; .0; 1; 0/; .0; 0; 1/; the tuples ˛1 WD .1; 1; 1/; ˛2 WD .0; 1; 0/; ˛3 WD .2; 0; 1/ also form a basis. To see this, first observe that for all c1 ; c2 ; c3 ; d1 ; d2 ; d3 2 Z, we have .d1 ; d2 ; d3 / D c1 ˛1 C c2 ˛2 C c3 ˛3 if and only if d1 D c1 C 2c3 ; d2 D c1 C c2 ; and d3 D c1 C c3 :
(13.1)
If (13.1) holds with d1 D d2 D d3 D 0, then subtracting the equation c1 C c3 D 0 from c1 C 2c3 D 0, we see that c3 D 0, from which it easily follows that c1 D c2 D 0. This shows that the family f˛i g3iD1 is linearly independent. To show that it spans Z3 , the reader may verify that for any given d1 ; d2 ; d3 2 Z, the values c1 WD
d1 C 2d3 ; c2 WD d1 C d2
2d3 ; c3 WD d1
d3
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Modules and vector spaces
satisfy (13.1). The family of tuples .1; 1; 1/; .0; 1; 0/; .1; 0; 1/ is not a basis, as it is linearly dependent: the third tuple is equal to the first minus the second. The family of tuples .1; 0; 12/; .0; 1; 30/; .0; 0; 18/ is linearly independent, but does not span Z3 : the last component of any Z-linear combination of these tuples must be divisible by gcd.12; 30; 18/ D 6. However, this family of tuples is a basis for the Q-module Q3 . Example 13.29. Consider again the submodule RŒX<` of RŒX, where ` 0, consisting of all polynomials of degree less than ` (see Example 13.11). Then fXi 1 g`iD1 is a basis for RŒX<` over R. Example 13.30. Consider again the ring E D RŒX=.f /, where f 2 RŒX with deg.f / D ` 0 and lc.f / 2 R . As in Example 13.4, we may naturally view E as a module over R. From the observations in Example 7.39, it is clear that f i 1 g`iD1 is a basis for E over R, where WD ŒXf 2 E. The next theorem highlights a critical property of bases: Theorem 13.14. If f˛i gniD1 is a basis for an R-module M , then the map W
Rn ! M .c1 ; : : : ; cn / 7! c1 ˛1 C C cn ˛n
is an R-module isomorphism. In particular, every element of M can be expressed in a unique way as c1 ˛1 C C cn ˛n , for c1 ; : : : ; cn 2 R. Proof. We already saw that is an R-linear map in Example 13.21. Since f˛i gniD1 is linearly independent, it follows that the kernel of is trivial, so that is injective. That is surjective follows immediately from the fact that f˛i gniD1 spans M . The following is an immediate corollary of this theorem: Theorem 13.15. Any two R-modules that have bases of the same size are isomorphic. The following theorems develops an important connection between bases and linear maps. Theorem 13.16. Let f˛i gniD1 be a basis for an R-module M , and let W M ! M 0 be an R-linear map. (i) is surjective if and only if f.˛i /gniD1 spans M 0 .
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369
(ii) is injective if and only if f.˛i /gniD1 is linearly independent. (iii) is an isomorphism if and only if f.˛i /gniD1 is a basis for M 0 . Proof. By the previous theorem, we know that every element of M can be written P uniquely as i ci ˛i , where the ci ’s are in R. Therefore, every element in Im P P can be expressed as . i ci ˛i / D i ci .˛i /. It follows that Im is equal to the subspace of M 0 spanned by f.˛i /gniD1 . From this, (i) is clear. P For (ii), consider a non-zero element i ci ˛i of M , so that not all ci ’s are zero. P P Now, i ci ˛i 2 Ker if and only if i ci .˛i / D 0M 0 , and thus, Ker is nontrivial if and only if f.˛i /gniD1 is linearly dependent. That proves (ii). (iii) follows from (i) and (ii). E XERCISE 13.13. Let M be an R-module. Suppose f˛i gniD1 is a linearly independent family of elements of M . Show that for every J f1; : : : ; ng, the subfamily f˛j gj 2J is also linearly independent. E XERCISE 13.14. Suppose W M ! M 0 is an R-linear map. Show that if f˛i gniD1 is a linearly dependent family of elements of M , then f.˛i /gniD1 is also linearly dependent. E XERCISE 13.15. Suppose W M ! M 0 is an injective R-linear map and that f˛i gniD1 is a linearly independent family of elements of M . Show that f.˛i /gniD1 is linearly independent. E XERCISE 13.16. Suppose that f˛i gniD1 spans an R-module M and that W M ! M 0 is an R-linear map. Show that: (a) is surjective if and only if f.˛i /gniD1 spans M 0 ; (b) if f.˛i /gniD1 is linearly independent, then is injective. 13.5 Vector spaces and dimension Throughout this section, F denotes a field. A module over a field is also called a vector space. In particular, an F -module is called an F -vector space, or a vector space over F . For vector spaces over F , one typically uses the terms subspace and quotient space, instead of (respectively) submodule and quotient module; likewise, one usually uses the terms F -vector space homomorphism, isomorphism and automorphism, as appropriate. We now develop the basic theory of dimension for finitely generated vector spaces. Recall that a vector space V over F is finitely generated if V D h˛1 ; : : : ; ˛n iF for some elements ˛1 ; : : : ; ˛n of V . The main results here are that
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every finitely generated vector space has a basis, and all bases have the same number of elements. Throughout the rest of this section, V denotes a vector space over F . We begin with a technical fact that will be used several times throughout this section: Theorem 13.17. Suppose that f˛i gniD1 is a linearly independent family of elements that spans a subspace W ¨ V , and that ˛nC1 2 V n W . Then f˛i gnC1 i D1 is also linearly independent. Proof. Suppose we have a linear relation 0V D c1 ˛1 C C cn ˛n C cnC1 ˛nC1 ; where the ci ’s are in F . We want to show that all the ci ’s are zero. If cnC1 ¤ 0, then we have ˛nC1 D
1 cnC1 .c1 ˛1 C C cn ˛n / 2 W;
contradicting the assumption that ˛nC1 … W . Therefore, we must have cnC1 D 0, and the linear independence of f˛i gniD1 implies that c1 D D cn D 0. The next theorem says that every finitely generated vector space has a basis, and in fact, any family that spans a vector space contains a subfamily that is a basis for the vector space. Theorem 13.18. Suppose f˛i gniD1 is a family of elements that spans V . Then for some subset J f1; : : : ; ng, the subfamily f˛j gj 2J is a basis for V . Proof. We prove this by induction on n. If n D 0, the theorem is clear, so assume n > 0. Consider the subspace W of V spanned by f˛i ginD11 . By the induction hypothesis, for some K f1; : : : ; n 1g, the subfamily f˛k gk2K is a basis for W . There are two cases to consider. Case 1: ˛n 2 W . In this case, W D V , and the theorem clearly holds with WD J K. Case 2: ˛n … W . We claim that setting J WD K [ fng, the subfamily f˛j gj 2J is a basis for V . Indeed, since f˛k gk2K is linearly independent, and ˛n … W , Theorem 13.17 immediately implies that f˛j gj 2J is linearly independent. Also, since f˛k gk2K spans W , it is clear that f˛j gj 2J spans W C h˛n iF D V . Theorem 13.19. If V is spanned by some family of n elements of V , then every family of n C 1 elements of V is linearly dependent. Proof. We prove this by induction on n. If n D 0, the theorem is clear, so assume that n > 0. Let f˛i gniD1 be a family that spans V , and let fˇi gnC1 i D1 be an arbitrary nC1 family of elements of V . We wish to show that fˇi gi D1 is linearly dependent.
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We know that ˇnC1 is a linear combination of the ˛i ’s, say, ˇnC1 D c1 ˛1 C C cn ˛n :
(13.2)
If all the ci ’s were zero, then we would have ˇnC1 D 0V , and so trivially, fˇi gnC1 i D1 is linearly dependent. So assume that some ci is non-zero, and for concreteness, say cn ¤ 0. Dividing equation (13.2) through by cn , it follows that ˛n is an F linear combination of ˛1 ; : : : ; ˛n 1 ; ˇnC1 . Therefore, h˛1 ; : : : ; ˛n
1 ; ˇnC1 iF
h˛1 ; : : : ; ˛n
1 iF
C h˛n iF D V:
Now consider the subspace W WD hˇnC1 iF and the quotient space V =W . Since the family of elements ˛1 ; : : : ; ˛n 1 ; ˇnC1 spans V , it is easy to see that fŒ˛i W gni D11 spans V =W ; therefore, by induction, fŒˇi W gniD1 is linearly dependent. This means that there exist d1 ; : : : ; dn 2 F , not all zero, such that d1 ˇ1 C C dn ˇn 0 .mod W /, which means that for some dnC1 2 F , we have d1 ˇ1 C C dn ˇn D dnC1 ˇnC1 . That proves that fˇi gnC1 i D1 is linearly dependent. An important corollary of Theorem 13.19 is the following: Theorem 13.20. If V is finitely generated, then any two bases have the same size. Proof. If one basis had more elements than another, then Theorem 13.19 would imply that the first basis was linearly dependent, which contradicts the definition of a basis. Theorem 13.20 allows us to make the following definition: Definition 13.21. If V is finitely generated, the common size of any basis is called the dimension of V , and is denoted dimF .V /. Note that from the definitions, we have dimF .V / D 0 if and only if V is the trivial vector space (i.e., V D f0V g). We also note that one often refers to a finitely generated vector space as a finite dimensional vector space. We shall give preference to this terminology from now on. To summarize the main results in this section up to this point: if V is finite dimensional, it has a basis, and any two bases have the same size, which is called the dimension of V . Theorem 13.22. Suppose that dimF .V / D n, and that f˛i gniD1 is a family of n elements of V . The following are equivalent: (i) f˛i gniD1 is linearly independent; (ii) f˛i gniD1 spans V ; (iii) f˛i gniD1 is a basis for V .
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Proof. Let W be the subspace of V spanned by f˛i gniD1 . First, let us show that (i) implies (ii). Suppose f˛i gniD1 is linearly independent. Also, by way of contradiction, suppose that W ¨ V , and choose ˛nC1 2 V n W . Then Theorem 13.17 implies that f˛i gnC1 i D1 is linearly independent. But then we have a linearly independent family of n C 1 elements of V , which is impossible by Theorem 13.19. Second, let us prove that (ii) implies (i). Let us assume that f˛i gniD1 is linearly dependent, and prove that W ¨ V . By Theorem 13.18, we can find a basis for W among the ˛i ’s, and since f˛i gniD1 is linearly dependent, this basis must contain strictly fewer than n elements. Hence, dimF .W / < dimF .V /, and therefore, W ¨V. The theorem now follows from the above arguments, and the fact that, by definition, (iii) holds if and only if both (i) and (ii) hold. We next examine the dimension of subspaces of finite dimensional vector spaces. Theorem 13.23. If V is finite dimensional, and W is a subspace of V , then W is also finite dimensional, and dimF .W / dimF .V /. Moreover, dimF .W / D dimF .V / if and only if W D V . Proof. Suppose dimF .V / D n. Consider the set S of all linearly independent families of the form f˛i gm i D1 , where m 0 and each ˛i is in W . The set S is certainly non-empty, as it contains the empty family. Moreover, by Theorem 13.19, every member of S must have at most n elements. Therefore, we may choose some particular element f˛i gm i D1 of S, where m is as large as possible. We claim that this family f˛i gm is a basis for W . By definition, f˛i gm i D1 i D1 is linearly independent and spans a subspace of W . If this is a proper subspace W 0 ¨ W , we can choose an element ˛mC1 2 W n W 0 , and by Theorem 13.17, the family f˛i gmC1 i D1 is linearly independent, and therefore, this family also belongs to S, contradicting the assumption that m is as large as possible. That proves that W is finite dimensional with dimF .W / dimF .V /. It remains to show that these dimensions are equal if and only if W D V . Now, if W D V , then clearly dimF .W / D dimF .V /. Conversely, if dimF .W / D dimF .V /, then by Theorem 13.22, any basis for W must already span V . Theorem 13.24. If V is finite dimensional, and W is a subspace of V , then the quotient space V =W is also finite dimensional, and dimF .V =W / D dimF .V /
dimF .W /:
Proof. Suppose that f˛i gniD1 spans V . Then it is clear that fŒ˛i W gniD1 spans V =W . By Theorem 13.18, we know that V =W has a basis of the form fŒ˛i W g`iD1 , where
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373
` n (renumbering the ˛i ’s as necessary). By Theorem 13.23, we know that W has a basis, say fˇj gjmD1 . The theorem will follow immediately from the following: Claim. The elements ˛1 ; : : : ; ˛` ; ˇ1 ; : : : ; ˇm
(13.3)
form a basis for V . To see that this family spans V , consider any element of V . Then since P fŒ˛i W g`iD1 spans V =W , we have i ci ˛i .mod W / for some c1 ; : : : ; c` 2 P m F . If we set ˇ WD , we have ˇ D i ci ˛i 2 W , then since fˇj gjP D1 spans WP P j dj ˇj for some d1 ; : : : ; dm 2 F , and hence D i ci ˛i C j dj ˇj . That proves that the family of elements (13.3) spans V . To prove this family is linearly P P independent, suppose we have a relation of the form i ci ˛i C j dj ˇj D 0V , where c1 ; : : : ; c` 2 F and d1 ; : : : ; dm 2 F . If any of the ci ’s were non-zero, this would contradict the assumption that fŒ˛i W g`iD1 is linearly independent. So assume that all the ci ’s are zero. If any of the dj ’s were non-zero, this would contradict the assumption that fˇj gjmD1 is linearly independent. Thus, all the ci ’s and dj ’s must be zero, which proves that the family of elements (13.3) is linearly independent. That proves the claim. Theorem 13.25. If V is finite dimensional, then every linearly independent family of elements of V can be extended to form a basis for V . Proof. One can prove this by generalizing the proof of Theorem 13.18. Alternatively, we can adapt the proof of the previous theorem. Let fˇj gjmD1 be a linearly independent family of elements that spans a subspace W of V . As in the proof of the previous theorem, if fŒ˛i W g`iD1 is a basis for the quotient space V =W , then the elements ˛1 ; : : : ; ˛` ; ˇ1 ; : : : ; ˇm form a basis for V . Example 13.31. Suppose that F is finite, say jF j D q, and that V is finite dimensional, say dimF .V / D n. Then clearly jV j D q n . If W is a subspace with dimF .W / D m, then jW j D q m , and by Theorem 13.24, dimF .V =W / D n m, and hence jV =W j D q n m . Just viewing V and W as additive groups, we know that the index of W in V is ŒV W W D jV =W j D jV j=jW j D q n m , which agrees with the above calculations. We next consider the relation between the notion of dimension and linear maps. First, observe that by Theorem 13.15, if two finite dimensional vector spaces have the same dimension, then they are isomorphic. The following theorem is the converse:
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Modules and vector spaces
Theorem 13.26. If V is of finite dimension n, and V is isomorphic to V 0 , then V 0 is also of finite dimension n. Proof. If f˛i gniD1 is a basis for V , then by Theorem 13.16, f.˛i /gniD1 is a basis for V 0 . Thus, two finite dimensional vector spaces are isomorphic if and only if they have the same dimension. We next illustrate one way in which the notion of dimension is particularly useful. In general, if we have a function f W A ! B, injectivity does not imply surjectivity, nor does surjectivity imply injectivity. If A and B are finite sets of equal size, then these implications do indeed hold. The following theorem gives us another important setting where these implications hold, with finite dimensionality playing the role corresponding to finite cardinality: Theorem 13.27. If W V ! V 0 is an F -linear map, and if V and V 0 are finite dimensional with dimF .V / D dimF .V 0 /, then we have: is injective if and only if is surjective. Proof. Let f˛i gniD1 be a basis for V . By Theorem 13.16, we know that is injective if and only if f.˛i /gniD1 is linearly independent, and that is surjective if and only if f.˛i /gniD1 spans V 0 . Moreover, by Theorem 13.22, we know that f.˛i /gniD1 is linearly independent if and only if it spans V 0 . The theorem now follows immediately. This theorem may be generalized as follows: Theorem 13.28. If V is finite dimensional, and W V ! V 0 is an F -linear map, then Im is a finite dimensional vector space, and dimF .V / D dimF .Im / C dimF .Ker /: Proof. As the reader may verify, this follows immediately from Theorem 13.24, together with Theorems 13.26 and 13.9. Intuitively, one way to think of Theorem 13.28 is as a “law of conservation” for dimension: any “dimensionality” going into that is not “lost” to the kernel of must show up in the image of . E XERCISE 13.17. Show that if V1 ; : : : ; Vn are finite dimensional vector spaces, P then V1 Vn has dimension niD1 dimF .Vi /.
13.5 Vector spaces and dimension
375
E XERCISE 13.18. Show that if V is a finite dimensional vector space with subspaces W1 and W2 , then dimF .W1 C W2 / D dimF .W1 / C dimF .W2 /
dimF .W1 \ W2 /:
E XERCISE 13.19. From the previous exercise, one might be tempted to think that a more general “inclusion/exclusion principle” for dimension holds. Determine if the following statement is true or false: if V is a finite dimensional vector space with subspaces W1 , W2 , and W2 , then dimF .W1 C W2 C W3 / D dimF .W1 / C dimF .W2 / C dimF .W3 / dimF .W1 \ W2 / dimF .W1 \ W3 / dimF .W2 \ W3 / C dimF .W1 \ W2 \ W3 /. E XERCISE 13.20. The theory of dimension for finitely generated vector spaces is quite elegant and powerful. There is a theory of dimension (of sorts) for modules over an arbitrary, non-trivial ring R, but it is much more awkward and limited. This exercise develops a proof of one aspect of this theory: if an R-module M has a basis at all, then any two bases have the same size. To prove this, we need the fact that any non-trivial ring has a maximal ideal (this was proved in Exercise 7.39 for countable rings). Let n; m be positive integers, let ˛1 ; : : : ; ˛m be elements of Rn , and let I be an ideal of R. n n can be ex(a) Show that if f˛i gm i D1 spans R , then every element of I pressed as c1 ˛1 C C cm ˛m , where c1 ; : : : ; cm belong to I .
(b) Show that if m > n and I is a maximal ideal, then there exist c1 ; : : : ; cm 2 R, not all in I , such that c1 ˛1 C C cm ˛m 2 I n . (c) From (a) and (b), deduce that if m > n, then f˛i gm i D1 cannot be a basis for n R . (d) From (c), conclude that any two bases for a given R-module M must have the same size.
14 Matrices
In this chapter, we discuss basic definitions and results concerning matrices. We shall start out with a very general point of view, discussing matrices whose entries lie in an arbitrary ring R. Then we shall specialize to the case where the entries lie in a field F , where much more can be said. One of the main goals of this chapter is to discuss “Gaussian elimination,” which is an algorithm that allows us to efficiently compute bases for the image and kernel of an F -linear map. In discussing the complexity of algorithms for matrices over a ring R, we shall treat a ring R as an “abstract data type,” so that the running times of algorithms will be stated in terms of the number of arithmetic operations in R. If R is a finite ring, such as Zm , we can immediately translate this into a running time on a RAM (in later chapters, we will discuss other finite rings and efficient algorithms for doing arithmetic in them). If R is, say, the field of rational numbers, a complete running time analysis would require an additional analysis of the sizes of the numbers that appear in the execution of the algorithm. We shall not attempt such an analysis here — however, we note that all the algorithms discussed in this chapter do in fact run in polynomial time when R D Q, assuming we represent rational numbers as fractions in lowest terms. Another possible approach for dealing with rational numbers is to use floating point approximations. While this approach eliminates the size problem, it creates many new problems because of round-off errors. We shall not address any of these issues here. 14.1 Basic definitions and properties Throughout this section, R denotes a ring. For positive integers m and n, an m n matrix A over a ring R is a rectangular
376
14.1 Basic definitions and properties
˙a
array
AD
a1n a2n :: :
am1 am2
amn
11
a21 :: :
a12 a22 :: :
377
;
where each entry aij in the array is an element of R; the element aij is called the .i; j / entry of A, which we denote by A.i; j /. For i D 1; : : : ; m, the i th row of A is .ai1 ; : : : ; ai n /;
˙a
which we denote by Rowi .A/, and for j D 1; : : : ; n, the j th column of A is 1j
a2j :: :
;
amj which we denote by Colj .A/. We regard a row of A as a 1n matrix, and a column of A as an m 1 matrix. The set of all m n matrices over R is denoted by Rmn . Elements of R1n are called row vectors (of dimension n) and elements of Rm1 are called column vectors (of dimension m). Elements of Rnn are called square matrices (of dimension n). We do not make a distinction between R1n and Rn ; that is, we view standard n-tuples as row vectors. We can define the familiar operations of matrix addition and scalar multiplication: If A; B 2 Rmn , then A C B is the m n matrix whose .i; j / entry is A.i; j / C B.i; j /. If c 2 R and A 2 Rmn , then cA is the m n matrix whose .i; j / entry is cA.i; j /. In addition, we define the m n zero matrix to be the m n matrix, all of whose entries are 0R ; we denote this matrix by 0mn . R Theorem 14.1. With addition and scalar multiplication as defined above, Rmn is an R-module. The matrix 0mn is the additive identity, and the additive inverse of R mn a matrix A 2 R is the m n matrix whose .i; j / entry is A.i; j /. Proof. To prove this, one first verifies that matrix addition is associative and commutative, which follows from the associativity and commutativity of addition in R.
378
Matrices
The claims made about the additive identity and additive inverses are also easily verified. These observations establish that Rmn is an abelian group. One also has to check that all of the properties in Definition 13.1 hold. We leave this to this reader. We can also define the familiar operation of matrix multiplication: If A 2 Rmn and B 2 Rnp , then AB is the m p matrix whose .i; k/ entry is n X A.i; j /B.j; k/: j D1
In addition, we define the n n identity matrix to be the matrix I 2 Rnn , where I.i; i / WD 1R and I.i; j / WD 0R for i ¤ j . Theorem 14.2. (i) Matrix multiplication is associative; that is, A.BC / D .AB/C for all A 2 Rmn , B 2 Rnp , and C 2 Rpq . (ii) Matrix multiplication distributes over matrix addition; that is, A.C CD/ D AC C AD and .A C B/C D AC C BC for all A; B 2 Rmn and C; D 2 Rnp . (iii) The n n identity matrix I 2 Rnn acts as a multiplicative identity; that is, AI D A and IB D B for all A 2 Rmn and B 2 Rnm ; in particular, CI D C D IC for all C 2 Rnn . (iv) Matrix and scalar multiplication associate; that is, c.AB/ D .cA/B D A.cB/ for all c 2 R, A 2 Rmn , and B 2 Rnp . Proof. All of these are trivial, except for (i), which requires just a bit of computation to show that the .i; `/ entry of both A.BC / and .AB/C is (verify) p n X X
A.i; j /B.j; k/C.k; `/:
j D1 kD1
Note that while matrix addition is commutative, matrix multiplication in general is not. Indeed, Theorems 14.1 and 14.2 imply that Rnn satisfies all the properties of a ring except for commutativity of multiplication. Some simple but useful facts to keep in mind are the following: If A 2 Rmn and B 2 Rnp , then the kth column of AB is equal to Av, where v D Colk .B/; also, the i th row of AB is equal to wB, where w D Rowi .A/.
14.1 Basic definitions and properties
379
If A 2 Rmn and u D .c1 ; : : : ; cm / 2 R1m , then uA D
m X
ci Rowi .A/:
i D1
In words: uA is a linear combination of the rows of A, with coefficients taken from the corresponding entries of u.
d
If A 2 Rmn and
1
vD
:: : dn
2 Rn1 ;
then Av D
n X
dj Colj .A/:
j D1
In words: Av is a linear combination of the columns of A, with coefficients taken from the corresponding entries of v. If A 2 Rmn , the transpose of A, denoted by A> , is defined to be the n m matrix whose .j; i/ entry is A.i; j /. Theorem 14.3. If A; B 2 Rmn , C 2 Rnp , and c 2 R, then (i) .A C B/> D A> C B > , (ii) .cA/> D cA> , (iii) .A> /> D A, (iv) .AC /> D C > A> . Proof. Exercise. If Ai is an ni ni C1 matrix, for i D 1; : : : ; k, then by associativity of matrix multiplication, we may write the product matrix A1 Ak , which is an n1 nkC1 matrix, without any ambiguity. For an n n matrix A, and a positive integer k, we write Ak to denote the product A A, where there are k terms in the product. Note that A1 D A. We may extend this notation to k D 0, defining A0 to be the n n identity matrix. One may readily verify the usual rules of exponent arithmetic: for non-negative integers k; `, we have .A` /k D Ak` D .Ak /` and Ak A` D AkC` : It is easy also to see that part (iv) of Theorem 14.3 implies that for all non-negative
380
Matrices
integers k, we have .Ak /> D .A> /k : Algorithmic issues For computational purposes, matrices are represented in the obvious way as arrays of elements of R. As remarked at the beginning of this chapter, we shall treat R as an “abstract data type,” and not worry about how elements of R are actually represented; in discussing the complexity of algorithms, we shall simply count “operations in R,” by which we mean additions, subtractions, and multiplications; we shall sometimes also include equality testing and computing multiplicative inverses as “operations in R.” In any real implementation, there will be other costs, such as incrementing counters, and so on, which we may safely ignore, as long as their number is at most proportional to the number of operations in R. The following statements are easy to verify: We can multiply an m n matrix times a scalar using mn operations in R. We can add two m n matrices using mn operations in R. We can multiply an m n matrix and an n p matrix using O.mnp/ operations in R. It is also easy to see that given an n n matrix A, and a non-negative integer e, we can adapt the repeated squaring algorithm discussed in §3.4 so as to compute Ae using O.len.e// multiplications of n n matrices, and hence O.len.e/n3 / operations in R. E XERCISE 14.1. Let A 2 Rmn . Show that if vA is the 1 n zero matrix for all v 2 R1m , then A is the m n zero matrix. 14.2 Matrices and linear maps Let R be a ring. For positive integers m and n, consider the R-modules R1m and R1n . If A is an m n matrix over R, then the map A W R1m ! R1n v 7! vA is easily seen to be an R-linear map — this follows immediately from parts (ii) and (iv) of Theorem 14.2. We call A the linear map corresponding to A.
14.2 Matrices and linear maps
381
If v D .c1 ; : : : ; cm / 2 R1m , then A .v/ D vA D
m X
ci Rowi .A/:
i D1
From this, it is clear that the image of A is the submodule of R1n spanned by fRowi .A/gm i D1 ; in m 1n particular, A is surjective if and only if fRowi .A/gi D1 spans R ; m A is injective if and only if fRowi .A/gi D1 is linearly independent. There is a close connection between matrix multiplication and composition of corresponding linear maps. Specifically, let A 2 Rmn and B 2 Rnp , and consider the corresponding linear maps A W R1m ! R1n and B W R1n ! R1p . Then we have B B A D AB :
(14.1)
This follows immediately from the associativity of matrix multiplication. We have seen how vector/matrix multiplication defines a linear map. Conversely, we shall now see that the action of any R-linear map can be viewed as a vector/matrix multiplication, provided the R-modules involved have bases (which will always be the case for finite dimensional vector spaces). Let M be an R-module, and suppose that S D f˛i gm i D1 is a basis for M , where m > 0. As we know (see Theorem 13.14), every element ˛ 2 M can be written uniquely as c1 ˛1 C C cm ˛m , where the ci ’s are in R. Let us define VecS .˛/ WD .c1 ; : : : ; cm / 2 R1m : We call VecS .˛/ the coordinate vector of ˛ relative to S. The function VecS W M ! R1m is an R-module isomorphism (it is the inverse of the isomorphism in Theorem 13.14). Let N be another R-module, and suppose that T D fˇj gjnD1 is a basis for N , where n > 0. Just as in the previous paragraph, every element ˇ 2 N has a unique coordinate vector VecT .ˇ/ 2 R1n relative to T . Now let W M ! N be an arbitrary R-linear map. Our goal is to define a matrix A 2 Rmn with the following property: VecT ..˛// D VecS .˛/A for all ˛ 2 M :
(14.2)
In words: if we multiply the coordinate vector of ˛ on the right by A, we get the coordinate vector of .˛/. Constructing such a matrix A is easy: we define A to be the matrix whose i th row, for i D 1; : : : ; m, is the coordinate vector of .˛i / relative to T . That is, Rowi .A/ D VecT ..˛i // for i D 1; : : : ; m.
382
Matrices
Then for an arbitrary ˛ 2 M , if .c1 ; : : : ; cm / is the coordinate vector of ˛ relative to S, we have X X ci ˛i D ci .˛i / .˛/ D i
i
and so VecT ..˛// D
X
ci VecT ..˛i // D
i
X
ci Rowi .A/ D VecS .˛/A:
i
Furthermore, A is the only matrix satisfying (14.2). Indeed, if A0 also satisfies (14.2), then subtracting, we obtain VecS .˛/.A
1n A0 / D 0R
for all ˛ 2 M . Since the map VecS W M ! R1m is surjective, this means that v.A A0 / is zero for all v 2 R1m , and from this, it is clear (see Exercise 14.1) that A A0 is the zero matrix, and so A D A0 . We call the unique matrix A satisfying (14.2) the matrix of relative to S and T , and denote it by MatS;T ./. Recall that HomR .M; N / is the R-module consisting of all R-linear maps from M to N (see Theorem 13.12). We can view MatS;T as a function mapping elements of HomR .M; N / to elements of Rmn . Theorem 14.4. The function MatS;T W HomR .M; N / ! Rmn is an R-module isomorphism. In particular, for every A 2 Rmn , the pre-image of A under MatS;T is VecT 1 BA B VecS , where A W R1m ! R1n is the linear map corresponding to A. Proof. To show that MatS;T is an R-linear map, let ; 0 2 HomR .M; N /, and let c 2 R. Also, let A WD MatS;T ./ and A0 WD MatS;T .0 /. Then for all ˛ 2 M , we have VecT .. C 0 /.˛// D VecT ..˛/ C 0 .˛// D VecT ..˛// C VecT .0 .˛// D VecS .˛/A C VecS .˛/A0 D VecS .˛/.A C A0 /: As this holds for all ˛ 2 M , and since the matrix of a linear map is uniquely determined, we must have MatS;T . C 0 / D A C A0 . A similar argument shows that MatS;T .c/ D cA. This shows that MatS;T is an R-linear map. To show that the map MatS;T is injective, it suffices to show that its kernel is trivial. If is in the kernel of this map, then setting A WD 0mn in (14.2), we see R that VecT ..˛// is zero for all ˛ 2 M . But since the map VecT W N ! R1n is injective, this implies .˛/ is zero for all ˛ 2 M . Thus, must be the zero map.
14.2 Matrices and linear maps
383
To show surjectivity, we show that every A 2 Rmn has an inverse as described in the statement of the theorem. So let A be an m n matrix, and let WD VecT 1 BA B VecS . Again, since the matrix of a linear map is uniquely determined, it suffices to show that (14.2) holds for this particular A and . For every ˛ 2 M , we have VecT ..˛// D VecT .VecT 1 .A .VecS .˛//// D A .VecS .˛// D VecS .˛/A: That proves the theorem. As a special case of the above, suppose that M D R1m and N D R1n , and S and T are the standard bases for M and N (see Example 13.27). In this case, the functions VecS and VecT are the identity maps, and the previous theorem implies that the function ƒ W Rmn ! HomR .R1m ; R1n / A 7! A is the inverse of the function MatS;T W HomR .R1m ; R1n / ! Rmn . Thus, the function ƒ is also an R-module isomorphism. To summarize, we see that an R-linear map from M to N , together with particular bases for M and N , uniquely determine a matrix A such that the action of multiplication on the right by A implements the action of with respect to the given bases. There may be many bases for M and N to choose from, and different choices will in general lead to different matrices. Also, note that in general, a basis may be indexed by an arbitrary finite set I ; however, in defining coordinate vectors and matrices of linear maps, the index set I must be ordered in some way. In any case, from a computational perspective, the matrix A gives us an efficient way to compute the map , assuming elements of M and N are represented as coordinate vectors with respect to the given bases. We have taken a “row-centric” point of view. Of course, if one prefers, by simply transposing everything, one can equally well take a “column-centric” point of view, where the action of corresponds to multiplication of a column vector on the left by a matrix. Example 14.1. Consider the quotient ring E D RŒX=.f /, where f 2 RŒX with deg.f / D ` > 0 and lc.f / 2 R . Let WD ŒXf 2 E. As an R-module, E has a basis S WD f i 1 g`iD1 (see Example 13.30). Let W E ! E be the multiplication map, which sends ˛ 2 E to ˛ 2 E. This is an R-linear map. If f D c0 C c1 X C C c` 1 X` 1 C c` X` , then the matrix of relative to S is the
384
Matrices
` ` matrix 0 0
1 0
:: :
0 1
AD 0 c0 =c`
0 0
;
0 c2 =c`
0 c1 =c`
1 c`
1 =c`
where for i D 1; : : : ; ` 1, the i th row of A contains a 1 in position i C 1, and is zero everywhere else. The matrix A is called the companion matrix of f . Example 14.2. Let x1 ; : : : ; xk 2 R. Let RŒX
is an R-linear map. Let T be the standard basis for R1k . Then the matrix of relative to S and T is the k k matrix
AD
1 x1 x12 :: : x1k
1 x2 x22 :: : 1
x2k
1
1 xk xk2 :: : xkk
: 1
The matrix A is called a Vandermonde matrix. E XERCISE 14.2. Let W M ! N and W N ! P be R-linear maps, and suppose that M , N , and P have bases S, T , and U, respectively. Show that MatS;U . B / D MatS;T . / MatT ;U . /: E XERCISE 14.3. Suppose V and W are finite dimensional vector spaces over a field F , and that f˛i gkiD1 is a linearly independent family of elements of V . Show that for all ˇ1 ; : : : ; ˇk 2 W , there exists 2 HomF .V; W / such that .˛i / D ˇi for i D 1; : : : ; k. E XERCISE 14.4. Let F be a finite field, and let A be a non-zero m n matrix over F . Suppose one chooses a vector v 2 F 1m at random. Show that the probability that vA is the zero vector is at most 1=jF j.
14.3 The inverse of a matrix
385
E XERCISE 14.5. Design and analyze a probabilistic algorithm that takes as input matrices A; B; C 2 Zpmm , where p is a prime. The algorithm should run in time O.m2 len.p/2 / and should output either “yes” or “no” so that the following holds: if C D AB, then the algorithm should always output “yes”; if C ¤ AB, then the algorithm should output “no” with probability at least 0:999. 14.3 The inverse of a matrix Let R be a ring. For a square matrix A 2 Rnn , we call a matrix B 2 Rnn an inverse of A if BA D AB D I , where I is the n n identity matrix. It is easy to see that if A has an inverse, then the inverse is unique: if B and C are inverses of A, then B D BI D B.AC / D .BA/C D IC D C: Because the inverse of A is uniquely determined, we denote it by A 1 . If A has an inverse, we say that A is invertible, or non-singular. If A is not invertible, it is sometimes called singular. We will use the terms “invertible” and “not invertible.” Observe that A is the inverse of A 1 ; that is, .A 1 / 1 D A. If A and B are invertible n n matrices, then so is their product: in fact, it is easy to see that .AB/ 1 D B 1 A 1 (verify). It follows that if A is an invertible matrix, and k is a non-negative integer, then Ak is invertible with inverse .A 1 /k , which we also denote by A k . It is also easy to see that A is invertible if and only if the transposed matrix A> is invertible, in which case .A> / 1 D .A 1 /> . Indeed, AB D I D BA holds if and only if B > A> D I D A> B > . We now develop a connection between invertible matrices and R-module isomorphisms. Recall from the previous section the R-module isomorphism ƒ W Rnn ! HomR .R1n ; R1n / A 7! A ; where for each A 2 Rnn , A is the corresponding R-linear map A W R1n ! R1n v 7! vA: Evidently, I is the identity map. Theorem 14.5. Let A 2 Rnn , and let A W R1n ! R1n be the corresponding R-linear map. Then A is invertible if and only if A is bijective.
386
Matrices
Proof. Suppose A is invertible, and that B is its inverse. We have AB D BA D I , and hence AB D BA D I , from which it follows (see (14.1)) that B B A D A B B D I . Since I is the identity map, this implies A is bijective. Suppose A is bijective, and that is its inverse. We know that is itself an R-linear map, and since the mapping ƒ above is surjective, we have D B for some B 2 Rnn . Therefore, we have B B A D A B B D I , and hence (again, see 14.1)) AB D BA D I . Since the mapping ƒ is injective, it follows that AB D BA D I . This implies A is invertible. We also have: Theorem 14.6. Let A 2 Rnn . The following are equivalent: (i) A is invertible; (ii) fRowi .A/gniD1 is a basis for R1n ; (iii) fColj .A/gjnD1 is a basis for Rn1 . Proof. We first prove the equivalence of (i) and (ii). By the previous theorem, A is invertible if and only if A is bijective. Also, in the previous section, we observed that A is surjective if and only if fRowi .A/gniD1 spans R1n , and that A is injective if and only if fRowi .A/gniD1 is linearly independent. The equivalence of (i) and (iii) follows by considering the transpose of A. E XERCISE 14.6. Let R be a ring, and let A be a square matrix over R. Let us call B a left inverse of A if BA D I , and let us call C a right inverse of A if AC D I . (a) Show that if A has both a left inverse B and a right inverse C , then B D C and hence A is invertible. (b) Assume that R is a field. Show that if A has either a left inverse or a right inverse, then A is invertible. Note that part (b) of the previous exercise holds for arbitrary rings, but the proof of this is non-trivial, and requires the development of the theory of determinants, which we do not cover in this text. E XERCISE 14.7. Show that if A and B are two square matrices over a field such that their product AB is invertible, then both A and B themselves must be invertible. E XERCISE 14.8. Show that if A is a square matrix over an arbitrary ring, and Ak is invertible for some k > 0, then A is invertible. E XERCISE 14.9. With notation as in Example 14.1, show that the matrix A is invertible if and only if c0 2 R .
387
14.4 Gaussian elimination
E XERCISE 14.10. With notation as in Example 14.2, show that the matrix A is invertible if and only if xi xj 2 R for all i ¤ j . 14.4 Gaussian elimination Throughout this section, F denotes a field. A matrix B 2 F mn is said to be in reduced row echelon form if there exists a sequence of integers .p1 ; : : : ; pr /, with 0 r m and 1 p1 < p2 < < pr n, such that the following holds: for i D 1; : : : ; r, all of the entries in row i of B to the left of entry .i; pi / are zero (i.e., B.i; j / D 0 for j D 1; : : : ; pi 1); for i D 1; : : : ; r, all of the entries in column pi of B above entry .i; pi / are zero (i.e., B.i 0 ; pi / D 0 for i 0 D 1; : : : ; i 1); for i D 1; : : : ; r, we have B.i; pi / D 1; all entries in rows r C 1; : : : ; m of B are zero (i.e., B.i; j / D 0 for i D r C 1; : : : ; m and j D 1; : : : ; n). It is easy to see that if B is in reduced row echelon form, the sequence .p1 ; : : : ; pr / above is uniquely determined, and we call it the pivot sequence of B. Several further remarks are in order: All of the entries of B are completely determined by the pivot sequence, except for the entries .i; j / with 1 i r and j > pi with j … fpi C1 ; : : : ; pr g, which may be arbitrary. If B is an n n matrix in reduced row echelon form whose pivot sequence is of length n, then B must be the n n identity matrix. We allow for an empty pivot sequence (i.e., r D 0), which will be the case precisely when B D 0mn . Example 14.3. The following 4 6 matrix B over the rational numbers is in reduced row echelon form:
0
BD
1 0 0 0 0 0 0
2 0 0 0
0 1 0 0
0 0 1 0
˘
3 2 : 4 0
The pivot sequence of B is .2; 4; 5/. Notice that the first three rows of B form a linearly independent family of vectors, that columns 2, 4, and 5 form a linearly independent family of vectors, and that all of other columns of B are linear combinations of columns 2, 4, and 5. Indeed, if we truncate the pivot columns to their first three rows, we get the 3 3 identity matrix.
388
Matrices
Generalizing the previous example, if a matrix is in reduced row echelon form, it is easy to deduce the following properties, which turn out to be quite useful: Theorem 14.7. If B is a matrix in reduced row echelon form with pivot sequence .p1 ; : : : ; pr /, then (i) rows 1; 2; : : : ; r of B form a linearly independent family of vectors; (ii) columns p1 ; : : : ; pr of B form a linearly independent family of vectors, and all other columns of B can be expressed as linear combinations of columns p1 ; : : : ; pr . Proof. Exercise—just look at the matrix! Gaussian elimination is an algorithm that transforms an arbitrary m n matrix A into a mn matrix B, where B is a matrix in reduced row echelon form obtained from A by a sequence of elementary row operations. There are three types of elementary row operations: Type I: swap two rows, Type II: multiply a row by a non-zero scalar, Type III: add a scalar multiple of one row to a different row. The application of any specific elementary row operation to an m n matrix C can be affected by multiplying C on the left by a suitable m m matrix M . Indeed, the matrix M corresponding to a particular elementary row operation is simply the matrix obtained by applying the same elementary row operation to the m m identity matrix. It is easy to see that for every elementary row operation, the corresponding matrix M is invertible. We now describe the basic version of Gaussian elimination. The input is an m n matrix A, and the algorithm is described in Fig. 14.1. The algorithm works as follows. First, it makes a copy B of A (this is not necessary if the original matrix A is not needed afterwards). The algorithm proceeds column by column, starting with the left-most column, so that after processing column j , the first j columns of B are in reduced row echelon form, and the current value of r represents the length of the pivot sequence. To process column j , in steps 3–6 the algorithm first searches for a non-zero element among B.r C 1; j /; : : : ; B.m; j /; if none is found, then the first j C 1 columns of B are already in reduced row echelon form. Otherwise, one of these non-zero elements is selected as the pivot element (the choice is arbitrary), which is then used in steps 8–13 to bring column j into the required form. After incrementing r, the pivot element is brought into position .r; j /, using a Type I operation in step 9. Then the entry .r; j / is set to 1, using a Type II operation in step 10. Finally, all the entries above and below entry .r; j / are set to 0, using Type III operations in steps 11–13.
389
14.4 Gaussian elimination
1. B A, r 0 2. for j 1 to n do 3. ` 0, i r 4. while ` D 0 and i m do 5. i i C1 6. if B.i; j / ¤ 0 then ` i 7. if ` ¤ 0 then 8. r r C1 9. swap rows r and ` of B 10. Rowr .B/ B.r; j / 1 Rowr .B/ 11. for i 1 to m do 12. if i ¤ r then 13. Rowi .B/ Rowi .B/ 14. output B
B.i; j / Rowr .B/
Fig. 14.1. Gaussian elimination Note that because columns 1; : : : ; j 1 of B were already in reduced row echelon form, none of these operations changes any values in these columns. As for the complexity of the algorithm, it is easy to see that it performs O.mn/ elementary row operations, each of which takes O.n/ operations in F , so a total of O.mn2 / operations in F . Example 14.4. Consider the execution of the Gaussian elimination algorithm on input AD
Œ0 Œ1 Œ1 Œ2 Œ1 Œ2 Œ2 Œ2 Œ0
2 Z33 3 :
After copying A into B, the algorithm transforms B as follows: Œ0 Œ1 Œ1 Œ2 Œ1 Œ2 Œ2 Œ2 Œ0
Row3
Row3
Row1 $Row2
!
Œ2 Row1
!
Œ2 Œ1 Œ2 Œ0 Œ1 Œ1 Œ2 Œ2 Œ0
Œ1 Œ2 Œ1 Œ0 Œ1 Œ1 Œ0 Œ1 Œ1
Row1
Œ2 Row1
!
Row1
Row1
Œ2 Row2
!
Œ1 Œ2 Œ1 Œ0 Œ1 Œ1 Œ2 Œ2 Œ0
Œ1 Œ0 Œ2 Œ0 Œ1 Œ1 Œ0 Œ1 Œ1
Œ2
390 Row3
Row3
Matrices
Œ1 Œ0 Œ0 Œ1 Œ1 Œ0 Œ0 Œ0
Row2
!
Suppose the Gaussian elimination algorithm performs a total of t elementary row operations. Then as discussed above, the application of the eth elementary row operation, for e D 1; : : : ; t , amounts to multiplying the current value of the matrix B on the left by a particular invertible m m matrix Me . Therefore, the final, output value of B satisfies the equation B D MA where M D M t M t
1 M1 :
Since the product of invertible matrices is also invertible, we see that M itself is invertible. Although the algorithm as presented does not compute the matrix M , it can be easily modified to do so. The resulting algorithm, which we call extended Gaussian elimination, is the same as plain Gaussian elimination, except that we initialize the matrix M to be the m m identity matrix, and we add the following steps: Just before step 9: swap rows r and ` of M . 1 Row .M /: r
Just before step 10: Rowr .M /
B.r; j /
Just before step 13: Rowi .M /
Rowi .M /
B.i; j / Rowr .M /:
At the end of the algorithm we output M in addition to B. So we simply perform the same elementary row operations on M that we perform on B. The reader may verify that the above algorithm is correct, and that it uses O.mn.m C n// operations in F . Example 14.5. Continuing with Example 14.4, the execution of the extended Gaussian elimination algorithm initializes M to the identity matrix, and then transforms M as follows: Œ1 Œ0 Œ0 Œ0 Œ1 Œ0 Œ0 Œ0 Œ1
Row3
Row3
Row1 $Row2
!
Œ2 Row1
!
Œ0 Œ1 Œ0 Œ1 Œ0 Œ0 Œ0 Œ0 Œ1
Œ0 Œ2 Œ0 Œ1 Œ0 Œ0 Œ0 Œ2 Œ1
Row1
Œ2 Row1
!
Row1
Row1
Œ2 Row2
!
Œ0 Œ2 Œ0 Œ1 Œ0 Œ0 Œ0 Œ0 Œ1
Œ1 Œ2 Œ0 Œ1 Œ0 Œ0 Œ0 Œ2 Œ1
Œ0
14.5 Applications of Gaussian elimination Row3
Row3
Row2
!
391
Œ1 Œ2 Œ1 Œ0 Œ0 Œ2 Œ2 Œ1
E XERCISE 14.11. For each type of elementary row operation, describe the matrix M which corresponds to it, as well as M 1 . E XERCISE 14.12. Given a matrix B 2 F mn in reduced row echelon form, show how to compute its pivot sequence using O.n/ operations in F . E XERCISE 14.13. In §4.4, we saw how to speed up matrix multiplication over Z using the Chinese remainder theorem. In this exercise, you are to do the same, but for performing Gaussian elimination over Zp , where p is a large prime. Suppose you are given an m m matrix A over Zp , where len.p/ D ‚.m/. Straightforward application of Gaussian elimination would require O.m3 / operations in Zp , each of which takes time O.m2 /, leading to a total running time of O.m5 /. Show how to use the techniques of §4.4 to reduce the running time of Gaussian elimination to O.m4 /. 14.5 Applications of Gaussian elimination Throughout this section, A is an arbitrary m n matrix over a field F , and MA D B, where M is an invertible mm matrix, and B is an mn matrix in reduced row echelon form with pivot sequence .p1 ; : : : ; pr /. This is precisely the information produced by the extended Gaussian elimination algorithm, given A as input (the pivot sequence can easily be “read” directly from B — see Exercise 14.12). Let V WD F 1m , W WD F 1n , and let A W V ! W v 7! vA be the linear map corresponding to A. Computing the image and kernel Consider first the row space of A, that is, the subspace of W spanned by fRowi .A/gm i D1 , which is equal to the image of A . We claim that the row space of A is the same as the row space of B. To see this, note that for every v 2 V , since B D MA, we have vB D v.MA/ D .vM /A, and so the row space of B is contained in the row space of A. For the other containment,
392
Matrices
note that since M is invertible, we can write A D M 1 B, and apply the same argument. Further, note that row space of B, and hence that of A, clearly has dimension r. Indeed, as stated in Theorem 14.7, rows 1; : : : ; r of B form a basis for the row space of B. Consider next the kernel K of A , or what we might call the row null space of A. We claim that fRowi .M /gm i DrC1 is a basis for K. Clearly, just from the fact that MA D B and the fact that rows r C 1; : : : ; m of B are zero, it follows that rows r C 1; : : : ; m of M are contained in K. Furthermore, as M is invertible, fRowi .M /gm i D1 is a basis for V (see Theorem 14.6). Thus, the family of vectors 0 fRowi .M /gm i DrC1 is linearly independent and spans a subspace K of K. It suffices to show that K 0 D K. Suppose to the contrary that K 0 ¨ K, and let v 2 K n K 0 . Pm As fRowi .M /gm i D1 ci Rowi .M /; moreover, as i D1 spans V , we may write v D 0 v … K , we must have ci ¤ 0 for some i D 1; : : : ; r. Setting vQ WD .c1 ; : : : ; cm /, we see that v D vM Q , and so A .v/ D vA D .vM Q /A D v.MA/ Q D vB: Q Furthermore, since fRowi .B/griD1 is linearly independent, rows r C 1; : : : ; m of B are zero, and vQ has a non-zero entry in one of its first r positions, we see that vB Q is not the zero vector. We have derived a contradiction, and hence may conclude that K 0 D K. Finally, note that if m D n, then A is invertible if and only if its row space has dimension m, which holds if and only if r D m, and in the latter case, B will be the identity matrix, and hence M is the inverse of A. Let us summarize the above discussion: The first r rows of B form a basis for the row space of A (i.e., the image of A ). The last m r rows of M form a basis for the row null space of A (i.e., the kernel of A ). If m D n, then A is invertible (i.e., A is an isomorphism) if and only if r D m, in which case M is the inverse of A (i.e., the matrix of A 1 relative to the standard basis). So we see that from the output of the extended Gaussian elimination algorithm, we can simply “read off” bases for both the image and the kernel, as well as the inverse (if it exists), of a linear map represented as a matrix with respect to some bases. Also note that this procedure provides a “constructive” version of Theorem 13.28.
14.5 Applications of Gaussian elimination
393
Example 14.6. Continuing with Examples 14.4 and 14.5, we see that the vectors .Œ1; Œ0; Œ2/ and .Œ0; Œ1; Œ1/ form a basis for the row space of A, while the vector .Œ2; Œ2; Œ1/ is a basis for the row null space of A. Solving linear systems of equations Suppose that in addition to the matrix A, we are given w 2 W , and want to find a solution v (or perhaps describe all solutions v), to the equation vA D w:
(14.3)
Equivalently, we can phrase the problem as finding an element (or describing all elements) of the set A 1 .w/. Now, if there exists a solution at all, say v 2 V , then A .v/ D A .v/ Q if and only if v vQ .mod K/, where K is the kernel of A . It follows that the set of all solutions to (14.3) is v C K D fv C v0 W v0 2 Kg. Thus, given a basis for K and any solution v to (14.3), we have a complete and concise description of the set of solutions to (14.3). As we have discussed above, the last m r rows of M form a basis for K, so it suffices to determine if w 2 Im A , and if so, determine a single pre-image v of w. Also as we discussed, Im A , that is, the row space of A, is equal to the row space of B, and because of the special form of B, we can quickly and easily determine if the given w is in the row space of B, as follows. By definition, w is in the row space of B if and only if there exists a vector vN 2 V such that vB N D w. We may as well assume that all but the first r entries of vN are zero. Moreover, vB N D w implies that for i D 1; : : : ; r, the i th entry of vN is equal to the pi th entry of w. Thus, the vector v, N if it exists, is completely determined by the entries of w at positions p1 ; : : : ; pr . We can construct vN satisfying these conditions, and then test if vB N D w. If not, then we may conclude that (14.3) has no solutions; otherwise, setting v WD vM N , we see that vA D .vM N /A D v.MA/ N D vB N D w, and so v is a solution to (14.3). One easily verifies that if we implement the above procedure as an algorithm, the work done in addition to running the extended Gaussian elimination algorithm amounts to O.m.n C m// operations in F . A special case of the above procedure is when m D n and A is invertible, in which case (14.3) has a unique solution, namely, v WD wM , since in this case, M D A 1.
394
Matrices
The rank of a matrix Define the row rank of A to be the dimension of its row space, which is dimF .Im A /, and define the column rank of A to be the dimension of its column space, that is, the subspace of F m1 spanned by fColj .A/gjnD1 . Now, the column space A may not be the same as the column space of B, but from the relation B D MA, and the fact that M is invertible, it easily follows that these two subspaces are isomorphic (via the isomorphism that sends x 2 F m1 to M x), and hence have the same dimension. Moreover, by Theorem 14.7, the column rank of B is r, which is the same as the row rank of A. So we may conclude: The column rank and row rank of A are the same. Because of this, we define the rank of a matrix to be the common value of its row and column rank. The orthogonal complement of a subspace So as to give equal treatment to rows and columns, one can also define the column null space of A to be the kernel of the linear map defined by multiplication on the left by A. By applying the results above to the transpose of A, we see that the column null space of A has dimension n r, where r is the rank of A. Let U W denote the row space of A, and let Ux W denote the set of all vectors uN 2 W whose transpose uN > belong to the column null space of A. Now, U is a subspace of W of dimension r and Ux is a subspace of W of dimension n r. Moreover, if U \ Ux D f0W g, then by Theorem 13.11 we have an isomorphism of U Ux with U C Ux , and since U Ux has dimension n, it must be the case that U C Ux D W . It follows that every element of W can be expressed uniquely as u C u, N where u 2 U and uN 2 Ux . Now, all of the conclusions in the previous paragraph hinged on the assumption that U \ Ux D f0W g. The space Ux consists precisely of all vectors uN 2 W which are “orthogonal” to all vectors u 2 U , in the sense that the “inner product” uuN > is zero. For this reason, Ux is sometimes called the “orthogonal complement of U .” The condition U \ Ux D f0W g is implied by the condition that U contains no non-zero “self-orthogonal vectors” u such that uu> is zero. If F is the field of real numbers, then of course there are no non-zero self-orthogonal vectors, since uu> is the sum of the squares of the entries of u. However, for other fields, there may very well be non-zero self-orthogonal vectors. As an example, if F D Z2 , then any vector u with an even number of 1-entries is self orthogonal. So we see that while much of the theory of vector spaces and matrices carries over without change from familiar ground fields, like the real numbers, to arbitrary ground fields F , not everything does. In particular, the usual decomposition of a
14.5 Applications of Gaussian elimination
395
vector space into a subspace and its orthogonal complement breaks down, as does any other procedure that relies on properties specific to “inner product spaces.” For the following three exercises, as above, A is an arbitrary m n matrix over a field F , and MA D B, where M is an invertible m m matrix, and B is in reduced row echelon form. E XERCISE 14.14. Show that the column null space of A is the same as the column null space of B. E XERCISE 14.15. Show how to compute a basis for the column null space of A using O.r.n r// operations in F , given A and B. E XERCISE 14.16. Show that the matrix B is uniquely determined by A; more precisely, show that if M 0 A D B 0 , where M 0 is an invertible m m matrix, and B 0 is in reduced row echelon form, then B 0 D B. In the following two exercises, the theory of determinants could be used; however, they can all be solved directly, without too much difficulty, using just the ideas developed so far in the text. E XERCISE 14.17. Let p be a prime. A matrix A 2 Zmm is called invertible modulo p if and only if there exists a matrix B 2 Zmm such that AB BA I .mod p/, where I is the m m integer identity matrix. Here, two matrices are considered congruent with respect to a given modulus if and only if their corresponding entries are congruent. Show that A is invertible modulo p if and only if A is invertible over Q, and the entries of A
1
lie in Q.p/ (see Example 7.26).
E XERCISE 14.18. You are given a matrix A 2 Zmm and a prime p such that A is invertible modulo p. Suppose that you are also given w 2 Z1m . (a) Show how to efficiently compute a vector v 2 Z1m such that vA D w .mod p/, and that v is uniquely determined modulo p. (b) Given a vector v as in part (a), along with an integer e 1, show how to efficiently compute vO 2 Z1m such that vA O D w .mod p e /, and that vO is uniquely determined modulo p e . Hint: mimic the “lifting” procedure discussed in §12.5.2. (c) Using parts (a) and (b), design and analyze an efficient algorithm that takes the matrix A and the prime p as input, together with a bound H on the absolute value of the numerator and denominator of the entries of the vector v 0 that is the unique (rational) solution to the equation v 0 A D w. Your
396
Matrices
algorithm should run in time polynomial in the length of H , the length of p, and the sum of the lengths of the entries of A and w. Hint: use rational reconstruction, but be sure to fully justify its application. Note that in the previous exercise, one can use the theory of determinants to derive good bounds, in terms of the lengths of the entries of A and w, on the size of the least prime p such that A is invertible modulo p (assuming A is invertible over the rationals), and the length of the numerator and denominator of the entries of rational solution v 0 to the equation v 0 A D w. The interested reader who is familiar with the basic theory of determinants is encouraged to establish such bounds. The next two exercises illustrate how Gaussian elimination can be adapted, in certain cases, to work in rings that are not necessarily fields. Let R be an arbitrary ring. A matrix B 2 Rmn is said to be in row echelon form if there exists a pivot sequence .p1 ; : : : ; pr /, with 0 r m and 1 p1 < p2 < < pr n, such that the following holds: for i D 1; : : : ; r, all of the entries in row i of B to the left of entry .i; pi / are zero; for i D 1; : : : ; r, we have B.i; pi / ¤ 0; all entries in rows r C 1; : : : ; m of B are zero. E XERCISE 14.19. Let R be the ring Zpe , where p is prime and e > 1. Let WD Œp 2 R. The goal of this exercise is to develop an efficient algorithm for the following problem: given a matrix A 2 Rmn , with m > n, find a vector v 2 R1m such that vA D 01n but v … R1m . (a) Show how to modify the extended Gaussian elimination algorithm to solve the following problem: given a matrix A 2 Rmn , compute M 2 Rmm and B 2 Rmn , such that MA D B, M is invertible, and B is in row echelon form. Your algorithm should run in time O.mn.m C n/e 2 len.p/2 /. Assume that the input includes the values p and e. Hint: when choosing a pivot element, select one divisible by a minimal power of ; as in ordinary Gaussian elimination, your algorithm should only use elementary row operations to transform the input matrix. (b) Using the fact that the matrix M computed in part (a) is invertible, argue that none of its rows belong to R1m . (c) Argue that if m > n and the matrix B computed in part (a) has pivot sequence .p1 ; : : : ; pr /, then m r > 0 and if v is any one of the last m r rows of M , then vA D 01n . (d) Give an example that shows that fRowi .B/griD1 need not be linearly independent, and that fRowi .M /gm i DrC1 need not span the kernel of the linear map A corresponding to A.
14.6 Notes
397
E XERCISE 14.20. Let R be the ring Z` , where ` > 1 is an integer. You are given a matrix A 2 Rmn . Show how to efficiently compute M 2 Rmm and B 2 Rmn such that MA D B, M is invertible, and B is in row echelon form. Your algorithm should run in time O.mn.m C n/ len.`/2 /. Hint: to zero-out entries, you should use “rotations”—for integers a; b; d; s; t with d D gcd.a; b/ ¤ 0 and as C bt D d; and for row indices r; i, a rotation simultaneously updates rows r and i of a matrix C as follows: b a .Rowr .C /; Rowi .C // .s Rowr .C /Ct Rowi .C /; Rowr .C /C Rowi .C //I d d observe that if C.r; j / D Œa` and C.i; j / D Œb` before applying the rotation, then C.r; j / D Œd ` and C.i; j / D Œ0` after the rotation. E XERCISE 14.21. Let fvi gniD1 be a family of vectors, where vi 2 R1` for i D 1; : : : ; n. We say that fvi gniD1 is pairwise orthogonal if vi vj> D 0 for all i ¤ j . Show that every pairwise orthogonal family of non-zero vectors over R is linearly independent. E XERCISE 14.22. The purpose of this exercise is to use linear algebra to prove that any pairwise independent family of hash functions (see §8.7) must contain a large number of hash functions. More precisely, let f r gr2R be a pairwise independent family of hash functions from S to T , with jT j 2. Our goal is to show that jRj jS j. Let n WD jS j, and m WD jT j, and ` WD jRj. Write R D fr1 ; : : : ; r` g and S D fs1 ; : : : ; sn g. Without loss of generality, we may assume that T is a set of nonzero real numbers that sum to zero (e.g., T D f1; : : : ; m 1; m.m 1/=2g). Now define the matrix M 2 Rn` with M.i; j / WD rj .si /. Show that fRowi .M /gniD1 is a pairwise orthogonal family of non-zero vectors (see previous exercise). From this, deduce that ` n. 14.6 Notes While a trivial application of the defining formulas yields a simple algorithm for multiplying two n n matrices over a ring R that uses O.n3 / operations in R, this algorithm is not the best, asymptotically speaking. The currently fastest algorithm for this problem, due to Coppersmith and Winograd [28], uses O.n! / operations in R, where ! < 2:376. We note, however, that the good old O.n3 / algorithm is still the only one used in almost any practical setting.
15 Subexponential-time discrete logarithms and factoring
This chapter presents subexponential-time algorithms for computing discrete logarithms and for factoring integers. These algorithms are based on a common technique, which makes essential use of the notion of a smooth number. 15.1 Smooth numbers If y is a non-negative real number, and m is a positive integer, then we say that m is y-smooth if all prime divisors of m are at most y. For 0 y x, let us define ‰.y; x/ to be the number of y-smooth integers up to x. The following theorem gives us a lower bound on ‰.y; x/, which will be crucial in the analysis of our discrete logarithm and factoring algorithms. Theorem 15.1. Let y be a function of x such that y log x ! 1 and u WD !1 log x log y as x ! 1. Then ‰.y; x/ x expŒ. 1 C o.1//u log log x: Proof. Let us write u D buc C ı, where 0 ı < 1. Let us split the primes up to y into two sets: the set V “very small” primes that are at most y ı =2, and the other primes W that are greater than y ı =2 but at most y. To simplify matters, let us also include the integer 1 in the set V . By Bertrand’s postulate (Theorem 5.8), there exists a constant C > 0 such that jW j Cy= log y for sufficiently large y. By the assumption that y= log x ! 1 as x ! 1, it follows that jW j 2buc for sufficiently large x. To derive the lower bound, we shall count those integers that can be built up by multiplying together buc distinct elements of W , together with one element of V .
398
15.2 An algorithm for discrete logarithms
399
These products are clearly distinct, y-smooth numbers, and each is bounded by x, since each is at most y buc y ı D y u D x. If S denotes the set of all of these products, then for x sufficiently large, we have ! jW j jS j D jV j buc jW j.jW j
D
jW j 2u
1/ .jW j bucŠ
buc C 1/
jV j
buc jV j
buc Cy jV j 2u log y u ı Cy jV j: D 2 log x
Taking logarithms, we have log jSj .u D log x
ı/.log y
log log x C log.C =2// C log jV j
u log log x C .log jV j
ı log y/ C
O.u C log log x/:
(15.1)
To prove the theorem, it suffices to show that log jS j log x
.1 C o.1//u log log x:
Under our assumption that u ! 1, the term O.u C log log x/ in (15.1) is o.u log log x/, and so it will suffice to show that the term log jV j ı log y is also o.u log log x/. But by Chebyshev’s theorem (Theorem 5.1), for some positive constant D, we have Dy ı = log y jV j y ı ; and taking logarithms, and again using the fact that u ! 1, we have log jV j
ı log y D O.log log y/ D o.u log log x/:
15.2 An algorithm for discrete logarithms We now present a probabilistic, subexponential-time algorithm for computing discrete logarithms. The input to the algorithm is p; q; ; ˛, where p and q are primes, with q j .p 1/, is an element of Zp generating a subgroup G of Zp of order q, and ˛ 2 G. We shall make the simplifying assumption that q 2 − .p 1/, which is equivalent
400
Subexponential-time discrete logarithms and factoring
to saying that q − m WD .p 1/=q. Although not strictly necessary, this assumption simplifies the design and analysis of the algorithm, and moreover, for cryptographic applications, this assumption is almost always satisfied. Exercises 15.1–15.3 below explore how this assumption may be lifted, as well as other generalizations. At a high level, the main goal of our discrete logarithm algorithm is to find a random representation of 1 with respect to and ˛ — as discussed in Exercise 11.12, this allows us to compute log ˛ (with high probability). More precisely, our main goal is to compute integers r and s in a probabilistic fashion, such that r ˛ s D 1 and Œsq is uniformly distributed over Zq . Having accomplished this, then with probability 1 1=q, we shall have s 6 0 .mod q/, which allows us to compute log ˛ as rs 1 mod q. Let G 0 be the subgroup of Zp of order m. Our assumption that q − m implies that G \ G 0 D f1g, since the multiplicative order of any element in the intersection must divide both q and m, and so the only possibility is that the multiplicative order is 1. Therefore, the map W G G 0 ! Zp that sends .ˇ; ı/ to ˇı is injective (Theorem 6.25), and since jZp j D q m, it must be surjective as well. We shall use this fact in the following way: if ˇ is chosen uniformly at random from G, and ı is chosen uniformly at random from G 0 (and independent of ˇ), then ˇı is uniformly distributed over Zp . Furthermore, since G 0 is the image of the q-power map on Zp , we may generate a random ı 2 G 0 simply by choosing ıO 2 Zp at random, and setting ı WD ıOq . The discrete logarithm algorithm uses a “smoothness parameter” y, whose choice will be discussed below when we analyze the running time of the algorithm; for now, we only assume that y < p. Let p1 ; : : : ; pk be an enumeration of the primes up to y. Let i WD Œpi p 2 Zp for i D 1; : : : ; k. The algorithm has two stages. In the first stage, we find relations of the form
ri ˛ si ıi D 1ei1 : : : keik ;
(15.2)
for integers ri ; si ; ei1 ; : : : ; ei k , and ıi 2 G 0 , and i D 1; : : : ; k C 1: We obtain one such relation by a randomized search, as follows: we choose ri ; si 2 f0; : : : ; q 1g at random, as well as ıOi 2 Zp at random; we then compute q ıi WD ıOi , ˇi WD ri ˛ si , and mi WD rep.ˇi ıi /. Now, the value ˇi is uniformly distributed over G, while ıi is uniformly distributed over G 0 ; therefore, the product ˇi ıi is uniformly distributed over Zp , and hence mi is uniformly distributed over f1; : : : ; p 1g. Next, we simply try to factor mi by trial division, trying all the primes p1 ; : : : ; pk up to y. If we are lucky, we completely factor mi in this way,
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15.2 An algorithm for discrete logarithms
obtaining a factorization mi D p1ei1 pkeik ; for some exponents ei1 ; : : : ; ei k , and we get the relation (15.2). If we are unlucky, then we simply keep trying until we are lucky. For i D 1; : : : ; kC1; let vi WD .ei1 ; : : : ; ei k / 2 Zk , and let vN i denote the image of vi in Zk N i WD .Œei1 q ; : : : ; Œei k q /). Since Zk q (i.e., v q is a vector space over the field Zq of dimension k, the family of vectors vN 1 ; : : : ; vN kC1 must be linearly dependent. The second stage of the algorithm uses Gaussian elimination over Zq (see §14.4) to find a linear dependence among the vectors vN 1 ; : : : ; vN kC1 , that is, to find integers c1 ; : : : ; ckC1 2 f0; : : : ; q 1g, not all zero, such that .e1 ; : : : ; ek / WD c1 v1 C C ckC1 vkC1 2 qZk : Raising each equation (15.2) to the power ci , and multiplying them all together, we obtain
r ˛ s ı D 1e1 kek ; where r WD
kC1 X i D1
ci ri ; s WD
kC1 X
ci si ; and ı WD
i D1
kC1 Y
ıici :
i D1 e
Now, ı 2 G 0 , and since each ej is a multiple of q, we also have j j 2 G 0 for j D 1; : : : ; k. It follows that r ˛ s 2 G 0 . But since r ˛ s 2 G as well, and G \ G 0 D f1g, it follows that r ˛ s D 1. If we are lucky (and we will be with overwhelming probability, as we discuss below), we will have s 6 0 .mod q/, in which case, we can compute s 0 WD s 1 mod q, obtaining ˛D
rs 0
;
and hence rs 0 mod q is the discrete logarithm of ˛ to the base . If we are very unlucky, we will have s 0 .mod q/, at which point the algorithm simply quits, reporting “failure.” The entire algorithm, called Algorithm SEDL, is presented in Fig. 15.1. As already argued above, if Algorithm SEDL does not output “failure,” then its output is indeed the discrete logarithm of ˛ to the base . There remain three questions to answer: 1. What is the expected running time of Algorithm SEDL? 2. How should the smoothness parameter y be chosen so as to minimize the expected running time? 3. What is the probability that Algorithm SEDL outputs “failure”?
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Subexponential-time discrete logarithms and factoring
i 0 repeat i i C1 repeat choose ri ; si 2 f0; : : : ; q 1g at random choose ıOi 2 Zp at random q ˇi
ri ˛ si , ıi ıOi , mi rep.ˇi ıi / test if mi is y-smooth (trial division) until mi D p1ei1 pkeik for some integers ei1 ; : : : ; ei k until i D k C 1 set vi
.ei1 ; : : : ; ei k / 2 Zk for i D 1; : : : ; k C 1
apply Gaussian elimination over Zq to find integers c1 ; : : : ; ckC1 2 f0; : : : ; q 1g, not all zero, such that c1 v1 C C ckC1 vkC1 2 qZk : PkC1 PkC1 r i D1 ci ri , s i D1 ci si if s 0 .mod q/ then output “failure” else output rs 1 mod q
Fig. 15.1. Algorithm SEDL Let us address these questions in turn. As for the expected running time, let be the probability that a random element of f1; : : : ; p 1g is y-smooth. Then the expected number of attempts needed to produce a single relation is 1 , and so the expected number of attempts to produce k C 1 relations is .k C 1/ 1 . In each attempt, we perform trial division using p1 ; : : : ; pk , along with a few other minor computations, leading to a total expected running time in stage 1 of k 2 1 len.p/O.1/ . The running time in stage 2 is dominated by the Gaussian elimination step, which takes time k 3 len.p/O.1/ . Thus, if Z is the total running time of the algorithm, then we have EŒZ .k 2
1
C k 3 / len.p/O.1/ :
(15.3)
Let us assume for the moment that y D expŒ.log p/Co.1/
(15.4)
for some constant with 0 < < 1. Our final choice of y will indeed satisfy this
15.2 An algorithm for discrete logarithms
403
assumption. Consider the probability . We have D ‰.y; p
1/ D ‰.y; p/=.p
1/=.p
1/ ‰.y; p/=p;
where for the second equality we use the assumption that y < p, so p is not ysmooth. With our assumption (15.4), we may apply Theorem 15.1 (with the given value of y and x WD p), obtaining expŒ. 1 C o.1//.log p= log y/ log log p: By Chebyshev’s theorem (Theorem 5.1), we know that k D ‚.y= log y/, and so log k D .1 C o.1// log y. Moreover, assumption (15.4) implies that the factor len.p/O.1/ in (15.3) is of the form expŒo.min.log y; log p= log y//, and so we have EŒZ expŒ.1 C o.1// maxf.log p= log y/ log log p C 2 log y; 3 log yg: (15.5)
Let us find the value of y that minimizes the right-hand side of (15.5), ignoring the “o(1)” terms. Let WD log y, A WD log p log log p, S1 WD A= C 2, and S2 WD 3. We want to find that minimizes maxfS1 ; S2 g. Using a little calculus, one seesp that S1 is minimized atp D .A=2/1=2 . With this choice of , we have S1 D .2 2/A1=2 and S2 D .3= 2/A1=2 < S1 . Thus, choosing p y D expŒ.1= 2/.log p log log p/1=2 ; we obtain
p
EŒZ expŒ.2 2 C o.1//.log p log log p/1=2 :
That takes care of the first two questions, although strictly speaking, we have only obtained an upper bound for the expected running time, and we have not shown that the choice of y is actually optimal, but we shall nevertheless content ourselves (for now) with these results. Finally, we deal with the third question, on the probability that the algorithm outputs “failure.” Lemma 15.2. The probability that Algorithm SEDL outputs “failure” is 1=q. Proof. Let F be the event that the algorithm outputs “failure.” For i D 1; : : : ; kC1, we may view the final values assigned to ri , si , ıi , and mi as random variables, which we shall denote by these same names (to avoid additional notation). Similarly, we may view s as a random variable. Let m01 ; : : : ; m0kC1 be arbitrary, fixed y-smooth numbers, and let H be the event that m1 D m01 ; : : : ; mkC1 D m0kC1 . We shall show that PŒF j H D 1=q, and since this holds for all relevant H, it follows by total probability that PŒF D 1=q. For the rest of the argument, we focus on the conditional distribution given H.
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Subexponential-time discrete logarithms and factoring
With respect to this conditional distribution, each random variable .ri ; si ; ıi / is essentially uniformly distributed over the set 0
0
Pi WD f.r 0 ; s 0 ; ı 0 / 2 Iq Iq G 0 W r ˛ s ı 0 D Œm0i p g; where Iq WD f0; : : : ; q 1g; moreover, the family of random variables is mutually independent. It is easy to see that for i D 1; : : : ; k C 1, f.ri ; si ; ıi /gikC1 D1 0 and for each s 2 Iq , there exist unique values r 0 2 Iq and ı 0 2 G 0 such that .r 0 ; s 0 ; ı 0 / 2 Pi . From this, it easily follows that each si is uniformly distributed over Iq , and the family of random variables fsi gkC1 i D1 is mutually independent. Also, the values c1 ; : : : ; ckC1 computed by the algorithm are fixed (as they are determined by m01 ; : : : ; m0kC1 ), and since s D c1 s1 C C ckC1 skC1 , and not all the ci ’s are zero modulo q, it follows that s mod q is uniformly distributed over Iq , and so is equal to zero with probability 1=q. Let us summarize the above discussion in the following theorem. Theorem 15.3. With the smoothness parameter set as p y WD expŒ.1= 2/.log p log log p/1=2 ; the expected running time of Algorithm SEDL is at most p expŒ.2 2 C o.1//.log p log log p/1=2 : The probability that Algorithm SEDL outputs “failure” is 1=q. In the description and analysis of Algorithm SEDL, we have assumed that the primes p1 ; : : : ; pk were pre-computed. Of course, we can construct this list of primes using, for example, the sieve of Eratosthenes (see §5.4), and the running time of this pre-computation will be dominated by the running time of Algorithm SEDL. In the analysis of Algorithm SEDL, we relied crucially on the fact that in generating a relation, each candidate element ri ˛ si ıi was uniformly distributed over Zp . If we simply left out the ıi ’s, then the candidate element would be uniformly distributed over the subgroup G, and Theorem 15.1 simply would not apply. Although the algorithm might anyway work as expected, we would not be able to prove this. E XERCISE 15.1. Using the result of Exercise 14.19, show how to modify Algorithm SEDL to work in the case where p 1 D q e m, e > 1, q − m, generates the subgroup G of Zp of order q e , and ˛ 2 G. Your algorithm should compute log ˛ with roughly the same expected running time and success probability as Algorithm SEDL.
15.2 An algorithm for discrete logarithms
405
E XERCISE 15.2. Using the algorithm of the previous exercise as a subroutine, design and analyze an algorithm for the following problem. The input is p; q; ; ˛, where p is a prime, q is a prime dividing p 1, generates the subgroup G of Zp of order q, and ˛ 2 G; note that we may have q 2 j .p 1/. The output is log ˛. Your algorithm should always succeed in computing this discrete logarithm, and its expected running time should be bounded by a constant times the expected running time of the algorithm of the previous exercise. E XERCISE 15.3. Using the result of Exercise 14.20, show how to modify Algorithm SEDL to solve the following problem: given a prime p, a generator for Zp , and an element ˛ 2 Zp , compute log ˛. Your algorithm should work without knowledge of the factorization of p 1; its expected running time should be roughly the same as that of Algorithm SEDL, but its success probability may be lower. In addition, explain how the success probability may be significantly increased at almost no cost by collecting a few extra relations. E XERCISE 15.4. Let n D pq, where p and q are distinct, large primes. Let e be a prime, with e < n and e − .p 1/.q 1/. Let x be a positive integer, with x < n. Suppose you are given n (but not its factorization!) along with e and x. In addition, you are given access to two “oracles,” which you may invoke as often as you like. The first oracle is a “challenge oracle”: each invocation of the oracle produces a “challenge” a 2 f1; : : : ; xg — distributed uniformly, and independent of all other challenges. The second oracle is a “solution oracle”: you invoke this oracle with the index of a previous challenge oracle; if the corresponding challenge was a, the solution oracle returns the eth root of a modulo n; that is, the solution oracle returns b 2 f1; : : : ; n 1g such that b e a .mod n/ — note that b always exists and is uniquely determined. Let us say that you “win” if you are able to compute the eth root modulo n of any challenge, but without invoking the solution oracle with the corresponding index of the challenge (otherwise, winning would be trivial, of course). (a) Design a probabilistic algorithm that wins the above game, using an expected number of expŒ.c C o.1//.log x log log x/1=2 len.n/O.1/ steps, for some constant c, where a “step” is either a computation step or an oracle invocation (either challenge or solution). Hint: Gaussian elimination over the field Ze . (b) Suppose invocations of the challenge oracle are “cheap,” while invocations
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Subexponential-time discrete logarithms and factoring
of the solution oracle are relatively “expensive.” How would you modify your strategy in part (a)? Exercise 15.4 has implications in cryptography. A popular way of implementing a public-key primitive known as a “digital signature” works as follows: to digitally sign a message M (which may be an arbitrarily long bit string), first apply a “hash function” or “message digest” H to M , obtaining an integer a in some fixed range f1; : : : ; xg, and then compute the signature of M as the eth root b of a modulo n. Anyone can verify that such a signature b is correct by checking that b e H.M / .mod n/; however, it would appear to be difficult to “forge” a signature without knowing the factorization of n. Indeed, one can prove the security of this signature scheme by assuming that it is hard to compute the eth root of a random number modulo n, and by making the heuristic assumption that H is a random function (see §15.5). However, for this proof to work, the value of x must be close to n; otherwise, if x is significantly smaller than n, as the result of this exercise, one can break the signature scheme at a cost that is roughly the same as the cost of factoring numbers around the size of x, rather than the size of n. 15.3 An algorithm for factoring integers We now present a probabilistic, subexponential-time algorithm for factoring integers. The algorithm uses techniques very similar to those used in Algorithm SEDL in §15.2. Let n > 1 be the integer we want to factor. We make a few simplifying assumptions. First, we assume that n is odd — this is not a real restriction, since we can always pull out any factors of 2 in a pre-processing step. Second, we assume that n is not a perfect power, that is, not of the form ab for integers a > 1 and b > 1— this is also not a real restriction, since we can always partially factor n using the algorithm from Exercise 3.31 if n is a perfect power. Third, we assume that n is not prime — this may be efficiently checked using, say, the Miller–Rabin test (see §10.2). Fourth, we assume that n is not divisible by any primes up to a “smoothness parameter” y — we can ensure this using trial division, and it will be clear that the running time of this pre-computation is dominated by that of the algorithm itself. With these assumptions, the prime factorization of n is of the form f
fw n D q1 1 qw ;
where w > 1, the qi ’s are distinct, odd primes, each greater than y, and the fi ’s are positive integers. The main goal of our factoring algorithm is to find a random square root of 1 in
15.3 An algorithm for factoring integers
407
Zn . Let W
Zn ! Z
f
q1 1
Œan 7! .Œa
Zq fw
f
q1 1
w
; : : : ; Œaq fw / w
be the ring isomorphism of the Chinese remainder theorem. The square roots of 1 in Zn are precisely those elements in 2 Zn such that . / D .˙1; : : : ; ˙1/. If
is a random square root of 1, then with probability 1 2 wC1 1=2, we have . / D . 1 ; : : : ; w /, where the i ’s are neither all 1 nor all 1 (i.e., ¤ ˙1). If this happens, then . 1/ D . 1 1; : : : ; w 1/, and so we see that some, but not all, of the values i 1 will be zero. The value of gcd.rep. 1/; n/ is f precisely the product of the prime powers qi i such that i 1 D 0, and hence this gcd will yield a non-trivial factorization of n, unless D ˙1. Let p1 ; : : : ; pk be the primes up to the smoothness parameter y mentioned above. Let i WD Œpi n 2 Zn for i D 1; : : : ; k. We first describe a simplified version of the algorithm, after which we modify the algorithm slightly to deal with a technical problem. Like Algorithm SEDL, this algorithm proceeds in two stages. In the first stage, we find relations of the form ˛i2 D 1ei1 keik ;
(15.6)
for ˛i 2 Zn , and i D 1; : : : ; k C 1. We can obtain such a relation by randomized search, as follows: we select ˛i 2 Zn at random, square it, and try to factor mi WD rep.˛i2 / by trial division, trying all the primes p1 ; : : : ; pk up to y. If we are lucky, we obtain a factorization mi D p1ei1 pkeik ; for some exponents ei1 ; : : : ; ei k , yielding the relation (15.6); if not, we just keep trying. For i D 1; : : : ; kC1, let vi WD .ei1 ; : : : ; ei k / 2 Zk , and let vN i denote the image of vi in Zk N i WD .Œei1 2 ; : : : ; Œei k 2 /). Since Zk 2 (i.e., v 2 is a vector space over the field Z2 of dimension k, the family of vectors vN 1 ; : : : ; vN kC1 must be linearly dependent. The second stage of the algorithm uses Gaussian elimination over Z2 to find a linear dependence among the vectors vN 1 ; : : : ; vN kC1 , that is, to find integers c1 ; : : : ; ckC1 2 f0; 1g, not all zero, such that .e1 ; : : : ; ek / WD c1 v1 C C ckC1 vkC1 2 2Zk : Raising each equation (15.6) to the power ci , and multiplying them all together, we obtain ˛ 2 D 1e1 kek ;
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Subexponential-time discrete logarithms and factoring
where ˛ WD
kC1 Y
˛ici :
i D1
Since each ei is even, we can compute e =2 e =2 ˇ WD 1 1 kk ;
and we see that ˛ 2 D ˇ 2 , and hence .˛=ˇ/2 D 1. Thus, WD ˛=ˇ is a square root of 1 in Zn . A more careful analysis (see below) shows that in fact, is uniformly distributed over all square roots of 1, and hence, with probability at least 1=2, if we compute gcd.rep. 1/; n/, we get a non-trivial factor of n. That is the basic idea of the algorithm. There is, however, a technical problem. Namely, in the method outlined above for generating a relation, we attempt to factor mi WD rep.˛i2 /. Thus, the running time of the algorithm will depend in a crucial way on the probability that a random square modulo n is y-smooth. Unfortunately for us, Theorem 15.1 does not say anything about this situation — it only applies to the situation where a number is chosen at random from an interval Œ1; x. There are (at least) three different ways to address this problem: 1. Ignore it, and just assume that the bounds in Theorem 15.1 apply to random squares modulo n (taking x WD n in the theorem). 2. Prove a version of Theorem 15.1 that applies to random squares modulo n. 3. Modify the factoring algorithm, so that Theorem 15.1 applies. The first choice, while not unreasonable from a practical point of view, is not very satisfying mathematically. It turns out that the second choice is a indeed a viable option (i.e., the theorem is true and is not so difficult to prove), but we opt for the third choice, as it is somewhat easier to carry out, and illustrates a probabilistic technique that is more generally useful. So here is how we modify the basic algorithm. Instead of generating relations of the form (15.6), we generate relations of the form ˛i2 ı D 1ei1 keik ;
(15.7)
for ı 2 Zn , ˛i 2 Zn , and i D 1; : : : ; k C 2. Note that the value ı is the same in all relations. We generate these relations as follows. For the very first relation (i.e., i D 1), we repeatedly choose ˛1 and ı in Zn at random, until rep.˛12 ı/ is y-smooth. Then, after having found the first relation, we find subsequent relations (i.e., for i > 1) by repeatedly choosing ˛i in Zn at random until rep.˛i2 ı/ is y-smooth, where ı is the same value that was used in the first relation. Now, Theorem 15.1 will apply
15.3 An algorithm for factoring integers
409
directly to determine the success probability of each attempt to generate the first relation. Having found this relation, the value ˛12 ı will be uniformly distributed over all y-smooth elements of Zn (i.e., elements whose integer representations are y-smooth). Consider the various cosets of .Zn /2 in Zn . Intuitively, it is much more likely that a random y-smooth element of Zn lies in a coset that contains many ysmooth elements, rather than a coset with very few, and indeed, it is reasonably likely that the fraction of y-smooth elements in the coset containing ı is not much less than the overall fraction of y-smooth elements in Zn . Therefore, for i > 1, each attempt to find a relation should succeed with reasonably high probability. This intuitive argument will be made rigorous in the analysis to follow. The second stage is then modified as follows. For i D 1; : : : ; k C 2, let .kC1/ . vi WD .ei1 ; : : : ; ei k ; 1/ 2 Z.kC1/ , and let vN i denote the image of vi in Z2 .kC1/ Since Z2 is a vector space over the field Z2 of dimension k C 1, the family of vectors vN 1 ; : : : ; vN kC2 must be linearly dependent. Therefore, we use Gaussian elimination over Z2 to find a linear dependence among the vectors vN 1 ; : : : ; vN kC2 , that is, to find integers c1 ; : : : ; ckC2 2 f0; 1g, not all zero, such that .e1 ; : : : ; ekC1 / WD c1 v1 C C ckC2 vkC2 2 2Z.kC1/ : Raising each equation (15.7) to the power ci , and multiplying them all together, we obtain ˛ 2 ı ekC1 D 1e1 kek ; where ˛ WD
kC2 Y
˛ici :
i D1
Since each ei is even, we can compute e =2 e =2 ˇ WD 1 1 kk ı
ekC1 =2
;
so that ˛ 2 D ˇ 2 and WD ˛=ˇ is a square root of 1 in Zn . The entire algorithm, called Algorithm SEF, is presented in Fig. 15.2. Now the analysis. From the discussion above, it is clear that Algorithm SEF either outputs “failure,” or outputs a non-trivial factor of n. So we have the same three questions to answer as we did in the analysis of Algorithm SEDL: 1. What is the expected running time of Algorithm SEF? 2. How should the smoothness parameter y be chosen so as to minimize the expected running time? 3. What is the probability that Algorithm SEF outputs “failure”?
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Subexponential-time discrete logarithms and factoring
i 0 repeat i i C1 repeat choose ˛i 2 Zn at random if i D 1 then choose ı 2 Zn at random mi rep.˛i2 ı/ test if mi is y-smooth (trial division) until mi D p1ei1 pkeik for some integers ei1 ; : : : ; ei k until i D k C 2 set vi
.ei1 ; : : : ; ei k ; 1/ 2 Z.kC1/ for i D 1; : : : ; k C 2
apply Gaussian elimination over Z2 to find integers c1 ; : : : ; ckC2 2 f0; 1g, not all zero, such that .e1 ; : : : ; ekC1 / WD c1 v1 C C ckC2 vkC2 2 2Z.kC1/ : QkC2 ci e =2 e =2 ˛=ˇ ˛ 1 1 kk ı ekC1 =2 , i D1 ˛i , ˇ if D ˙1 then output “failure” else output gcd.rep.
1/; n/
Fig. 15.2. Algorithm SEF To answer the first question, let denote the probability that (the canonical representative of) a random element of Zn is y-smooth. For i D 1; : : : ; k C 2, let Li denote the number iterations of the inner loop in the i th iteration of the main loop in stage 1; that is, Li is the number of attempts made in finding the i th relation. Lemma 15.4. For i D 1; : : : ; k C 2, we have EŒLi
1:
Proof. We first compute EŒL1 . As ı is chosen uniformly from Zn and independent of ˛1 , at each attempt to find a relation, ˛12 ı is uniformly distributed over Zn , and hence the probability that the attempt succeeds is precisely . This means EŒL1 D 1 . We next compute EŒLi for i > 1. To this end, let us denote the cosets of .Zn /2 by Zn as C1 ; : : : ; C t . As it happens, t D 2w , but this fact plays no role in the analysis. For j D 1; : : : ; t , let j denote the probability that a random element of Cj is y-smooth, and let j denote the probability that the final value of ı belongs to Cj .
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15.3 An algorithm for factoring integers
We claim that for j D 1; : : : ; t , we have j D j 1 t 1 . To see this, note that each coset Cj has the same number of elements, namely, jZn jt 1 , and so the number of y-smooth elements in Cj is equal to j jZn jt 1 . Moreover, the final value of ˛12 ı is equally likely to be any one of the y-smooth numbers in Zn , of which there are jZn j, and hence j D
j jZn jt jZn j
1
D j
1
t
1
;
which proves the claim. Now, for a fixed value of ı and a random choice of ˛i 2 Zn , one sees that ˛i2 ı is uniformly distributed over the coset containing ı. Therefore, for j D 1; : : : ; t , if j > 0, we have EŒLi j ı 2 Cj D j 1 :
Summing over all j D 1; : : : ; t with j > 0, it follows that X EŒLi j ı 2 Cj PŒı 2 Cj EŒLi D j >0
D
X
j
1
j D
X
j
1
j
1
t
1
1
;
j >0
j >0
which proves the lemma. So in stage 1, the expected number of attempts made in generating a single relation is 1 , each such attempt takes time klen.n/O.1/ , and we have to generate k C 2 relations, leading to a total expected running time in stage 1 of 1 k 2 len.n/O.1/ . Stage 2 is dominated by the cost of Gaussian elimination, which takes time k 3 len.n/O.1/ . Thus, if Z is the total running time of the algorithm, we have EŒZ .
1 2
k C k 3 / len.n/O.1/ :
By our assumption that n is not divisible by any primes up to y, all y-smooth integers up to n 1 are in fact relatively prime to n. Therefore, the number of ysmooth elements of Zn is equal to ‰.y; n 1/, and since n itself is not y-smooth, this is equal to ‰.y; n/. From this, it follows that D ‰.y; n/=jZn j ‰.y; n/=n: The rest of the running time analysis is essentially the same as in the analysis of Algorithm SEDL; that is, assuming y D expŒ.log n/Co.1/ for some constant 0 < < 1, we obtain EŒZ expŒ.1 C o.1// maxf.log n= log y/ log log n C 2 log y; 3 log yg: (15.8)
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Subexponential-time discrete logarithms and factoring
p Setting y D expŒ.1= 2/.log n log log n/1=2 , we obtain p EŒZ expŒ.2 2 C o.1//.log n log log n/1=2 : That basically takes care of the first two questions. As for the third, we have: Lemma 15.5. The probability that Algorithm SEF outputs “failure” is 2 1=2.
wC1
Proof. Let F be the event that the algorithm outputs “failure.” We may view the final values assigned to ı and ˛1 ; : : : ; ˛kC2 as random variables, which we shall 0 denote by these same names. Let ı 0 2 Zn and ˛10 ; : : : ; ˛kC2 2 .Zn /2 be arbitrary, 0 0 fixed values such that rep.˛i ı / is y-smooth for i D 1; : : : ; k C 2. Let H be the event that ı D ı 0 and ˛i2 D ˛i0 for i D 1; : : : ; k C 2. We shall show that PŒF j H D 2 wC1 , and since this holds for all relevant H, it follows by total probability that PŒF D 2 wC1 . For the rest of the argument, we focus on the conditional distribution given H. With respect to this conditional distribution, each random variable ˛i is essentially uniformly distributed over 1 .f˛i0 g/, where is the squaring map on Zn . Moreover, the family of random variables f˛i gikC2 D1 is mutually independent. Also, the values ˇ and c1 ; : : : ; ckC2 computed by the algorithm are fixed. It follows (see Exercise 8.13) that ˛ is essentially uniformly distributed over 1 .fˇ 2 g/, and hence
WD ˛=ˇ is a random square root of 1 in Zn . Thus, D ˙1 with probability 2 wC1 . Let us summarize the above discussion in the following theorem. Theorem 15.6. With the smoothness parameter set as p y WD expŒ.1= 2/.log n log log n/1=2 ; the expected running time of Algorithm SEF is at most p expŒ.2 2 C o.1//.log n log log n/1=2 : The probability that Algorithm SEF outputs “failure” is at most 1=2. E XERCISE 15.5. It is perhaps a bit depressing that after all that work, Algorithm SEF only succeeds (in the worst case) with probability 1=2. Of course, to reduce the failure probability, we can simply repeat the entire computation — with ` repetitions, the failure probability drops to 2 ` . However, there is a better way to reduce the failure probability. Suppose that in stage 1, instead of collecting k C 2 relations, we collect k C 1 C ` relations, where ` 1 is an integer parameter.
413
15.4 Practical improvements
(a) Show that in stage 2, we can use Gaussian elimination over Z2 to find integer vectors .j /
.j /
c .j / D .c1 ; : : : ; ckC1C` / 2 f0; 1g.kC1C`/ .j D 1; : : : ; `/ such that – over the field Z2 , the images of the vectors c .1/ ; : : : ; c .`/ in .kC1C`/ Z2 form a linearly independent family of vectors, and – for j D 1; : : : ; `, we have .j /
.j /
c1 v1 C C ckC1C` vkC1C` 2 2Z.kC2/ : (b) Show that given vectors c .1/ ; : : : ; c .`/ as in part (a), if for j D 1; : : : ; `, we set .j /
.j /
.j /
˛
.j /
kC1C` Y
c
.j /
˛i i ; ˇ .j /
.j /
c1 v1 C C ckC1C` vkC1C` ;
.e1 ; : : : ; ekC1 / e
.j /
1 1
=2
e
.j /
kk
=2
.j /
ı
ekC1 =2
; .j /
˛ .j /=ˇ .j /;
i D1
then the family of random variables .1/ ; : : : ; .`/ is mutually independent, with each .j / uniformly distributed over the set of all square roots of 1 in Zn , and hence at least one of gcd.rep. .j / 1/; n/ splits n with probability at least 1 2 ` . So, for example, if we set ` D 20, then the failure probability is reduced to less than one in a million, while the increase in running time over Algorithm SEF will hardly be noticeable. 15.4 Practical improvements Our presentation and analysis of algorithms for discrete logarithms and factoring were geared towards simplicity and mathematical rigor. However, if one really wants to compute discrete logarithms or factor numbers, then a number of important practical improvements should be considered. In this section, we briefly sketch some of these improvements, focusing our attention on algorithms for factoring numbers (although some of the techniques apply to discrete logarithms as well). 15.4.1 Better smoothness density estimates From an algorithmic point of view, the simplest way to improve the running times of both Algorithms SEDL and SEF is to use a more accurate smoothness density
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Subexponential-time discrete logarithms and factoring
estimate, which dictates a different choice of the smoothness bound y in those algorithms, speeding them up significantly. While our Theorem 15.1 is a valid lower bound on the density of smooth numbers, it is not “tight,” in the sense that the actual density of smooth numbers is somewhat higher. We quote from the literature the following result: Theorem 15.7. Let y be a function of x such that for some > 0, we have y D ..log x/1C / and u WD
log x !1 log y
as x ! 1. Then ‰.y; x/ D x expŒ. 1 C o.1//u log u: Proof. See §15.5. Let us apply this result to the analysis of Algorithm SEF. Assume that y D expŒ.log n/1=2Co.1/ — our choice of y will in fact be of this form. With this assumption, we have log log y D .1=2 C o.1// log log n, and using Theorem 15.7, we can improve the inequality (15.8), obtaining instead (verify) EŒZ expŒ.1 C o.1// maxf.1=2/.log n= log y/ log log n C 2 log y; 3 log yg:
From this, if we set y WD expŒ.1=2/.log n log log n/1=2 /; we obtain EŒZ expŒ.2 C o.1//.log n log log n/1=2 :
An analogous improvement can be obtained for Algorithm SEDL. p Although this improvement reduces the constant 2 2 2:828 to 2, the constant is in the exponent, and so this improvement is not to be scoffed at! 15.4.2 The quadratic sieve algorithm We now describe a practical improvement to Algorithm SEF. This algorithm, known as the quadratic sieve, is faster in practice than Algorithm SEF; however, its analysis is somewhat heuristic. First, let us return to the simplified version of Algorithm SEF, where we collect relations of the form (15.6). Furthermore, instead of choosing the values ˛i at random, we will choose them in a special way, as we now describe. Let p nQ WD b nc;
15.4 Practical improvements
415
and define the polynomial F WD .X C n/ Q 2
n 2 ZŒX:
In addition to the usual “smoothness parameter” y, we need a “sieving parameter” z, whose choice will be discussed below. We shall assume that both y and z are of the form expŒ.log n/1=2Co.1/ , and our ultimate choices of y and z will indeed satisfy this assumption. For all s D 1; 2; : : : ; bzc, we shall determine which values of s are “good,” in the sense that the corresponding value F .s/ is y-smooth. For each good s, since we have F .s/ .s C n/ Q 2 .mod n/, we obtain one relation of the form (15.6), with ˛i WD Œs C n Q n . If we find at least k C 1 good values of s, then we can apply Gaussian elimination as usual to find a square root of 1 in Zn . Hopefully, we will have ¤ ˙1, allowing us to split n. Observe that for 1 s z, we have 1 F .s/ z 2 C 2zn1=2 n1=2Co.1/ : Now, although the values F .s/ are not at all random, we might expect heuristically that the number of good s up to z is roughly equal to O z, where O is the probability that a random integer in the interval Œ1; n1=2 is y-smooth, and by Theorem 15.7, we have O D expŒ. 1=4 C o.1//.log n= log y/ log log n: If our heuristics are valid, this already gives us an improvement over Algorithm SEF, since now we are looking for y-smooth numbers near n1=2 , which are much more common than y-smooth numbers near n. But there is another improvement possible; namely, instead of testing each individual number F .s/ for smoothness using trial division, we can test them all at once using the following “sieving procedure.” The sieving procedure works as follows. First, we create an array vŒ1 : : : bzc, and initialize vŒs to F .s/, for 1 s z. Then, for each prime p up to y, we do the following: 1. Compute the roots of the polynomial F modulo p. This can be done quite efficiently, as follows. For p D 2, F has exactly one root modulo p, which is determined by the parity of n. Q For p > 2, we may use the familiar quadratic formula together with an algorithm for computing square roots modulo p, as discussed in Exercise 12.7. A quick calculation shows that the discriminant of F is n, and thus, F has a root modulo p if and only if n is a quadratic residue modulo p, in which case it will have two roots (under our usual assumptions, we cannot have p j n).
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Subexponential-time discrete logarithms and factoring
2. Assume that F has vp distinct roots modulo p lying in the interval Œ1; p; call them r1 ; : : : ; rvp . Note that vp D 1 for p D 2 and vp 2 f0; 2g for p > 2. Also note that F .s/ 0 .mod p/ if and only if s ri .mod p/ for some i D 1; : : : ; vp . For i D 1; : : : ; vp , do the following: s ri while s z do repeat vŒs s sCp
vŒs=p until p − vŒs
At the end of this sieving procedure, the good values of s may be identified as precisely those such that vŒs D 1. The running time of this sieving procedure is at most len.n/O.1/ times X z X 1 Dz D O.z log log y/ D z 1Co.1/ : p p py py Here, we have made use of Theorem 5.10, although this is not really necessary — P for our purposes, the bound py 1=p D O.log y/ would suffice. Note that this sieving procedure is a factor of k 1Co.1/ faster than the method for finding smooth numbers based on trial division. With just a little extra book-keeping, we can not only identify the good values of s, but we can also compute the factorization of F .s/ into primes, at essentially no extra cost. Now, let us put together all the pieces. We have to choose z just large enough so as to find at least k C 1 good values of s up to z. So we should choose z so that z k=O — in practice, we could choose an initial estimate for z, and if this choice of z does not yield enough relations, we could keep doubling z until we do get enough relations. Assuming that z k=O , the cost of sieving is .k=O /1Co.1/ , or expŒ.1 C o.1//.1=4/.log n= log y/ log log n C log y: The cost of Gaussian elimination is still O.k 3 /, or expŒ.3 C o.1// log y: Thus, the total running time is bounded by expŒ.1 C o.1// maxf.1=4/.log n= log y/ log log n C log y; 3 log yg: Let WD log y, A WD .1=4/ log n log log n, S1 WD A= C and S2 WD 3, and let us find the value of that minimizes maxfS1 ; S2 g. Using a little calculus, one finds that S1 is minimized at D A1=2 . For this value of , we have S1 D 2A1=2
15.4 Practical improvements
417
and S2 D 3A1=2 > S1 , and so this choice of is a bit larger than optimal. For < A1=2 , S1 is decreasing (as a function of ), while S2 is always increasing. It follows that the optimal value of is obtained by setting A= C D 3; and solving for . This yields D .A=2/1=2 . So setting p y WD expŒ.1=2 2/.log n log log n/1=2 ; the total running time of the quadratic sieve factoring algorithm is bounded by p expŒ.3=2 2 C o.1//.log n log log n/1=2 : Thus, we have reduced the constant in the exponent from 2, for p Algorithm SEF (using the more accurate smoothness density estimates), to 3=2 2 1:061. We mention one final improvement. The matrix to which we apply Gaussian elimination in stage 2 is “sparse”; indeed, since any integer less than n has O.log n/ prime factors, the total number of non-zero entries in the matrix is k 1Co.1/ . There are special algorithms for working with such sparse matrices, which allow us to perform stage 2 of the factoring algorithm in time k 2Co.1/ , or expŒ.2 C o.1// log y: Setting y WD expŒ.1=2/.log n log log n/1=2 ; the total running time is bounded by expŒ.1 C o.1//.log n log log n/1=2 :
p Thus, this improvement reduces the constant in the exponent from 3=2 2 1:061 to 1. Moreover, the special algorithms designed to work with sparse matrices typically use much less space than ordinary Gaussian elimination (even if the input to Gaussian elimination is sparse, the intermediate matrices will not be). We shall discuss in detail later, in §18.4, one such algorithm for solving sparse systems of linear equations. The quadratic sieve may fail to factor n, for one of two reasons: first, it may fail to find k C 1 relations; second, it may find these relations, but in stage 2, it only finds a trivial square root of 1. There is no rigorous theory to say why the algorithm should not fail for one of these two reasons, but experience shows that the algorithm does indeed work as expected.
418
Subexponential-time discrete logarithms and factoring
15.5 Notes Many of the algorithmic ideas in this chapter were first developed for the problem of factoring integers, and then later adapted to the discrete logarithm problem. The first (heuristic) subexponential-time algorithm for factoring integers, called the continued fraction method (not discussed here), was introduced by Lehmer and Powers [58], and later refined and implemented by Morrison and Brillhart [68]. The first rigorously analyzed subexponential-time algorithm for factoring integers was introduced by Dixon [35]. Algorithm SEF is a variation of Dixon’s algorithm, which works the same way as Algorithm SEF, except that it generates relations of the form (15.6) directly (and indeed, it is possible to prove a variant of Theorem 15.1, and for that matter, Theorem 15.7, for random squares modulo n). Algorithm SEF is based on an idea suggested by Rackoff (personal communication). Theorem 15.7 was proved by Canfield, Erd˝os, and Pomerance [23]. The quadratic sieve was introduced by Pomerance [76]. Recall that the quadratic sieve has a heuristic running time of expŒ.1 C o.1//.log n log log n/1=2 : This running time bound can also be achieved rigorously by a result of Lenstra and Pomerance [60], and to date, this is the best rigorous running time bound for factoring algorithms. We should stress, however, that most practitioners in this field are not so much interested in rigorous running time analyses as they are in actually factoring integers, and for such purposes, heuristic running time estimates are quite acceptable. Indeed, the quadratic sieve is much more practical than the algorithm in [60], which is mainly of theoretical interest. There are two other factoring algorithms not discussed here, but that should anyway at least be mentioned. The first is the elliptic curve method, introduced by Lenstra [59]. Unlike all of the other known subexponential-time algorithms, the running time of this algorithm is sensitive to the sizes of the factors of n; in particular, if p is the smallest prime dividing n, the algorithm will find p (heuristically) in expected time p expŒ. 2 C o.1//.log p log log p/1=2 len.n/O.1/ : This algorithm is quite practical, and is the method of choice when it is known (or suspected) that n has some small factors. It also has the advantage that it uses only polynomial space (unlike all of the other known subexponential-time factoring algorithms). The second is the number field sieve, the basic idea of which was introduced by Pollard [75], and later generalized and refined by Buhler, Lenstra, and Pomerance [21], as well as by others. The number field sieve will split n (heuristically) in
15.5 Notes
419
expected time expŒ.c C o.1//.log n/1=3 .log log n/2=3 ; where c is a constant (currently, the smallest value of c is 1:902, a result due to Coppersmith [27]). The number field sieve is currently the asymptotically fastest known factoring algorithm (at least, heuristically), and it is also practical, having been used to set the latest factoring record — the factorization of a 200-decimaldigit integer that is the product of two primes of about the same size. See the web page www.crypto-world.com/FactorRecords.html for more details (as well as for announcements of new records). As for subexponential-time algorithms for discrete logarithms, Adleman [1] adapted the ideas used for factoring to the discrete logarithm problem, although it seems that some of the basic ideas were known much earlier. Algorithm SEDL is a variation on this algorithm, and the basic technique is usually referred to as the index calculus method. The basic idea of the number field sieve was adapted to the discrete logarithm problem by Gordon [41]; see also Adleman [2] and Schirokauer, Weber, and Denny [82]. For many more details and references for subexponential-time algorithms for factoring and discrete logarithms, see Chapter 6 of Crandall and Pomerance [30]. Also, see the web page www.crypto-world.com/FactorWorld.html for links to research papers and implementation reports. For more details regarding the security of signature schemes, as discussed following Exercise 15.4, see the paper by Bellare and Rogaway [13]. Last, but not least, we should mention the fact that there are in fact polynomialtime algorithms for factoring and discrete logarithms; however, these algorithms require special hardware, namely, a quantum computer. Shor [90, 91] showed that these problems could be solved in polynomial time on such a device; however, at the present time, it is unclear when and if such machines will ever be built. Much, indeed most, of modern-day cryptography will crumble if this happens, or if efficient “classical” algorithms for these problems are discovered (which is still a real possibility).
16 More rings
This chapter develops a number of more advanced concepts concerning rings. These concepts will play important roles later in the text, and we prefer to discuss them now, so as to avoid too many interruptions of the flow of subsequent discussions. 16.1 Algebras Throughout this section, R denotes a ring (i.e., a commutative ring with unity). Sometimes, a ring may also be naturally viewed as an R-module, in which case, both the theory of rings and modules may be brought to bear to study its properties. Definition 16.1. An R-algebra is a set E, together with addition and multiplication operations on E, and a function W R E ! E, such that (i) with respect to addition and multiplication, E forms a ring; (ii) with respect to addition and the scalar multiplication map , E forms an R-module; (iii) for all c 2 R, and ˛; ˇ 2 E, we have .c; ˛/ˇ D .c; ˛ˇ/ D ˛.c; ˇ/: An R-algebra E may also be called an algebra over R. As we usually do for R-modules, we shall write c˛ instead of .c; ˛/. When we do this, part (iii) of the definition states that .c˛/ˇ D c.˛ˇ/ D ˛.cˇ/ for all c 2 R and ˛; ˇ 2 E. In particular, we may write c˛ˇ without any ambiguity. Note that there are two multiplication operations at play here: scalar multiplication (such as c˛), and ring multiplication (such as ˛ˇ). Also note that since we are assuming E is commutative, the second equality in part (iii) is already implied 420
16.1 Algebras
421
by the first. A simple consequence of the definition is that for all c; d 2 R and ˛; ˇ 2 E, we have .c˛/.dˇ/ D .cd /.˛ˇ/. From this, it follows that for all c 2 R, ˛ 2 E, and k 0, we have .c˛/k D c k ˛ k . Example 16.1. Suppose E is a ring and W R ! E is a ring homomorphism. With scalar multiplication defined by c˛ WD .c/˛ for c 2 R and ˛ 2 E, one may easily check that E is indeed an R-algebra. In this case, we say that E is an R-algebra via the map . Example 16.2. If R is a subring of E, then with W R ! E being the inclusion map, we can view E as an R-algebra as in the previous example. In this case, we say that E is an R-algebra via inclusion. Example 16.3. If W R ! E is a natural embedding of rings, then by a slight abuse of terminology, just as we sometimes say that R is a subring of E, we shall also say that E is an R-algebra via inclusion. In fact, all R-algebras can be viewed as special cases of Example 16.1: Theorem 16.2. If E is an R-algebra, then the map W R!E c 7! c 1E ; is a ring homomorphism, and c˛ D .c/˛ for all c 2 R and ˛ 2 E. Proof. Exercise. In the special situation where R is a field, we can say even more. In this situation, and with as in the above theorem, then either E is trivial or is injective (see Exercise 7.46). In the latter case, E contains an isomorphic copy of R as a subring. To summarize: Theorem 16.3. If R is a field, then an R-algebra is either the trivial ring or contains an isomorphic copy of R as a subring. The following examples give further important constructions of R-algebras. Example 16.4. If E1 ; : : : ; Ek are R-algebras, then so is their direct product E1 Ek , where addition, multiplication, and scalar multiplication are defined component-wise. If E D E1 D D Ek , we write this as E k . Example 16.5. If I is an arbitrary set, and E is an R-algebra, then Map.I; E/, which is the set of all functions f W I ! E, may be naturally viewed as an Ralgebra, with addition, multiplication, and scalar multiplication defined point-wise.
422
More rings
Example 16.6. Let E be an R-algebra and let I be an ideal of E. Then it is easily verified that I is also a submodule of E. This means that the quotient ring E=I may also be viewed as an R-module, and indeed, it is an R-algebra, called the quotient algebra (over R) of E modulo I . For ˛; ˇ 2 E and c 2 R, addition, multiplication, and scalar multiplication in E are defined as follows: Œ˛I C ŒˇI WD Œ˛ C ˇI ; Œ˛I ŒˇI WD Œ˛ ˇI ; c Œ˛I WD Œc ˛I : Example 16.7. The ring of polynomials RŒX is an R-algebra via inclusion. Let f 2 RŒX be a non-zero polynomial with lc.f / 2 R . We may form the quotient ring E WD RŒX=.f /, which may naturally be viewed as an R-algebra, as in the previous example. If deg.f / D 0, then E is trivial; so assume deg.f / > 0, and consider the map W R!E c 7! c 1E from Theorem 16.2. By definition, .c/ D Œcf , and this map is a natural embedding of rings. Just as we did in Example 7.55, we can identify R with its image in E under , and so view R as a subring of E; therefore, we can also view E as an R-algebra via inclusion. Subalgebras Let E be an R-algebra. A subset S of E is called a subalgebra (over R) of E if it is both a subring of E and a submodule of E. Of course, by restricting addition, multiplication, and scalar multiplication operations to a subalgebra S , we may view S as an R-algebra in its own right. The following theorem gives a simple characterization of subalgebras: Theorem 16.4. If E is an R-algebra via inclusion, and S is a subring of E, then S is a subalgebra if and only if S contains R. More generally, if E is an arbitrary R-algebra, and S is a subring of E, then S is a subalgebra of E if and only if S contains c 1E for all c 2 R. Proof. Exercise. R-algebra homomorphisms Let E and E 0 be R-algebras. A function W E ! E 0 is called an R-algebra homomorphism if is both a ring homomorphism and an R-linear map. As usual, if is bijective, then it is called an R-algebra isomorphism, and if, in addition, E D E 0 , it is called an R-algebra automorphism.
16.1 Algebras
423
The following theorem gives a simple characterization of R-algebra homomorphisms: Theorem 16.5. If E and E 0 are R-algebras via inclusion, and W E ! E 0 is a ring homomorphism, then is an R-algebra homomorphism if and only if the restriction of to R is the identity map. More generally, if E and E 0 are arbitrary R-algebras and W E ! E 0 is a ring homomorphism, then is an R-algebra homomorphism if and only if .c 1E / D c 1E 0 for all c 2 R. Proof. Exercise. Example 16.8. If E is an R-algebra and I is an ideal of E, then as observed in Example 16.6, I is also a submodule of E, and we may form the quotient algebra E=I . The natural map W E ! E=I ˛ 7! Œ˛I is both a ring homomorphism and an R-linear map, and hence is an R-algebra homomorphism. Example 16.9. Since C contains R as a subring, we may naturally view C as an R-algebra via inclusion. The complex conjugation map on C that sends a C bi to a bi, for a; b 2 R, is an R-algebra automorphism on C (see Example 7.5). Many simple facts about R-algebra homomorphisms can be obtained by combining corresponding facts for ring and R-module homomorphisms. For example, the composition of two R-algebra homomorphisms is again an R-algebra homomorphism, since the composition is both a ring homomorphism and an R-linear map (Theorems 7.22 and 13.6). As another example, if W E ! E 0 is an Ralgebra homomorphism, then its image S 0 is both a subring and a submodule of E 0 , and hence, S 0 is a subalgebra of E 0 . The kernel K of is an ideal of E, and we may form the quotient algebra E=K. The first isomorphism theorems for rings and modules (Theorems 7.26 and 13.9) tell us that E=K and S 0 are isomorphic both as rings and as R-modules, and hence, they are isomorphic as R-algebras. Specifically, the map N W E=K ! E 0 Œ˛K 7! .˛/ is an injective R-algebra homomorphism whose image is S 0 .
424
More rings
Polynomial evaluation Let E be an R-algebra. Consider the ring of polynomials RŒX (which is an Ralgebra via inclusion). Any polynomial g 2 RŒX naturally defines a function on P E: if g D i ai Xi , with each ai 2 R, and ˛ 2 E, then X g.˛/ WD ai ˛ i : i
Just as for rings, we say that ˛ is a root of g if g.˛/ D 0E . For fixed ˛ 2 E, the polynomial evaluation map W RŒX ! E g 7! g.˛/ is easily seen to be an R-algebra homomorphism. The image of is denoted RŒ˛, and is a subalgebra of E. Indeed, RŒ˛ is the smallest subalgebra of E containing ˛, and is called the subalgebra (over R) generated by ˛. Note that if E is an R-algebra via inclusion, then the notation RŒ˛ has the same meaning as that introduced in Example 7.44. We next state a very simple, but extremely useful, fact: Theorem 16.6. Let W E ! E 0 be an R-algebra homomorphism. Then for all g 2 RŒX and ˛ 2 E, we have .g.˛// D g..˛//: ai Xi 2 RŒX. Then we have X X X X .g.˛// D . ai ˛ i / D .ai ˛ i / D ai .˛ i / D ai .˛/i
Proof. Let g D
P
i
i
i
i
i
D g..˛//: As a special case of Theorem 16.6, if E D RŒ˛ for some ˛ 2 E, then every element of E can be expressed as g.˛/ for some g 2 RŒX, and .g.˛// D g..˛//; hence, the action of is completely determined by its action on ˛. Example 16.10. Let f 2 RŒX be a non-zero polynomial with lc.f / 2 R . As in Example 16.7, we may form the quotient algebra E WD RŒX=.f /. Observe that E D RŒ, where WD ŒXf . Now let E 0 be any R-algebra, and suppose that W E ! E 0 is an R-algebra homomorphism, and let 0 WD ./. The map sends g./ to g. 0 /, for g 2 RŒX. Thus, the image of is RŒ 0 . Also, since f ./ D 0E , we have 0E 0 D .f .// D f . 0 /. Thus, 0 must be a root of f . Conversely, suppose that 0 2 E 0 is a root of f . Then the polynomial evaluation map from RŒX to E 0 that sends g 2 RŒX to g. 0 / 2 E 0 is an R-algebra
16.2 The field of fractions of an integral domain
425
homomorphism whose kernel contains f . Using the generalized versions of the first isomorphism theorems for rings and R-modules (Theorems 7.27 and 13.10), we obtain the R-algebra homomorphism W
E ! E0 g./ 7! g. 0 /:
One sees that complex conjugation is just a special case of this construction (see Example 7.57). E XERCISE 16.1. Let E be an R-algebra, let ˛ 2 E, and consider the ˛multiplication map on E, which sends ˇ 2 E to ˛ˇ 2 E. Show that this map is an R-linear map. E XERCISE 16.2. Show that every ring may be viewed in a unique way as a Zalgebra, and that subrings are subalgebras, and ring homomorphisms are Z-algebra homomorphisms. E XERCISE 16.3. Show that the only R-algebra homomorphisms from C into itself are the identity map and the complex conjugation map. 16.2 The field of fractions of an integral domain Let D be an integral domain. Just as we can construct the field of rational numbers by forming fractions involving integers, we can construct a field consisting of fractions whose numerators and denominators are elements of D. This construction is quite straightforward, though a bit tedious. To begin with, let S be the set of all pairs of the form .a; b/, with a; b 2 D and b ¤ 0D . Intuitively, such a pair .a; b/ is a “formal fraction,” with numerator a and denominator b. We define a binary relation on S as follows: for .a1 ; b1 /; .a2 ; b2 / 2 S , we say .a1 ; b1 / .a2 ; b2 / if and only if a1 b2 D a2 b1 . Our first task is to show that this is an equivalence relation: Lemma 16.7. For all .a1 ; b1 /; .a2 ; b2 /; .a3 ; b3 / 2 S , we have (i) .a1 ; b1 / .a1 ; b1 /; (ii) .a1 ; b1 / .a2 ; b2 / implies .a2 ; b2 / .a1 ; b1 /; (iii) .a1 ; b1 / .a2 ; b2 / and .a2 ; b2 / .a3 ; b3 / implies .a1 ; b1 / .a3 ; b3 /. Proof. (i) and (ii) are rather trivial, and we do not comment on these any further. As for (iii), assume that a1 b2 D a2 b1 and a2 b3 D a3 b2 . Multiplying the first equation by b3 , we obtain a1 b2 b3 D a2 b1 b3 and substituting a3 b2 for a2 b3 on the right-hand side of this last equation, we obtain a1 b2 b3 D a3 b2 b1 . Now, using the
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fact that b2 is non-zero and that D is an integral domain, we may cancel b2 from both sides, obtaining a1 b3 D a3 b1 . Since is an equivalence relation, it partitions S into equivalence classes, and for .a; b/ 2 S, we denote by Œa; b the equivalence class containing .a; b/, and we denote by K the set of all such equivalence classes. Our next task is to define addition and multiplication operations on equivalence classes, mimicking the usual rules of arithmetic with fractions. We want to define the sum of Œa1 ; b1 and Œa2 ; b2 to be Œa1 b2 C a2 b1 ; b1 b2 , and the product of Œa1 ; b1 and Œa2 ; b2 to be Œa1 a2 ; b1 b2 . Note that since D is an integral domain, if b1 and b2 are non-zero, then so is the product b1 b2 , and therefore Œa1 b2 C a2 b1 ; b1 b2 and Œa1 a2 ; b1 b2 are indeed equivalence classes. However, to ensure that this definition is unambiguous, and does not depend on the particular choice of representatives of the equivalence classes Œa1 ; b1 and Œa2 ; b2 , we need the following lemma. Lemma 16.8. Let .a1 ; b1 /; .a10 ; b10 /; .a2 ; b2 /; .a20 ; b20 / 2 S , such that .a1 ; b1 / .a10 ; b10 / and .a2 ; b2 / .a20 ; b20 /. Then we have .a1 b2 C a2 b1 ; b1 b2 / .a10 b20 C a20 b10 ; b10 b20 / and .a1 a2 ; b1 b2 / .a10 a20 ; b10 b20 /: Proof. This is a straightforward calculation. Assume that a1 b10 D a10 b1 and a2 b20 D a20 b2 . Then we have .a1 b2 C a2 b1 /b10 b20 D a1 b2 b10 b20 C a2 b1 b10 b20 D a10 b2 b1 b20 C a20 b1 b10 b2 D .a10 b20 C a20 b10 /b1 b2 and a1 a2 b10 b20 D a10 a2 b1 b20 D a10 a20 b1 b2 : In light of this lemma, we may unambiguously define addition and multiplication on K as follows: for Œa1 ; b1 ; Œa2 ; b2 2 K, we define Œa1 ; b1 C Œa2 ; b2 WD Œa1 b2 C a2 b1 ; b1 b2 and Œa1 ; b1 Œa2 ; b2 WD Œa1 a2 ; b1 b2 : The next task is to show that K is a ring — we leave the details of this (which are quite straightforward) to the reader. Lemma 16.9. With addition and multiplication as defined above, K is a ring, with additive identity Œ0D ; 1D and multiplicative identity Œ1D ; 1D .
16.2 The field of fractions of an integral domain
427
Proof. Exercise. Finally, we observe that K is in fact a field: it is clear that Œa; b is a non-zero element of K if and only if a ¤ 0D , and hence any non-zero element Œa; b of K has a multiplicative inverse, namely, Œb; a. The field K is called the field of fractions of D. Consider the map W D ! K that sends a 2 D to Œa; 1D 2 K. It is easy to see that this map is a ring homomorphism, and one can also easily verify that it is injective. So, starting from D, we can synthesize “out of thin air” its field of fractions K, which essentially contains D as a subring, via the natural embedding W D ! K. Now suppose that we are given a field L that contains D as a subring. Consider the set K 0 consisting of all elements of L of the form ab 1 , where a; b 2 D and b ¤ 0D — note that here, the arithmetic operations are performed using the rules for arithmetic in L. One may easily verify that K 0 is a subfield of L that contains D, and it is easy to see that this is the smallest subfield of L that contains D. The subfield K 0 of L may be referred to as the field of fractions of D within L. One may easily verify that the map W K ! L that sends Œa; b 2 K to ab 1 2 L is an unambiguously defined ring homomorphism that maps K injectively onto K 0 . If we view K and L as D-algebras via inclusion, and we see that the map is in fact a D-algebra homomorphism. Thus, K and K 0 are isomorphic as D-algebras. It is in this sense that the field of fractions K is the smallest field that contains D as a subring. From now on, we shall simply write an element Œa; b of K as the fraction a=b. In this notation, the above rules for addition, multiplication, and testing equality in K now look quite familiar: a1 a2 a1 b2 C a2 b1 C D ; b1 b2 b1 b2
a1 a2 a1 a2 D ; b1 b2 b1 b2
a1 a2 D ” a1 b2 D a2 b1 : b1 b2
Function fields An important special case of the above construction for the field of fractions of D is when D D F ŒX, where F is a field. In this case, the field of fractions is denoted F .X/, and is called the field of rational functions (over F ). This terminology is a bit unfortunate, since just as with polynomials, although the elements of F .X/ define functions, they are not (in general) in one-to-one correspondence with these functions. Since F ŒX is a subring of F .X/, and since F is a subring of F ŒX, we see that F is a subfield of F .X/.
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More generally, we may apply the above construction to the ring D D F ŒX1 ; : : : ; Xn of multi-variate polynomials over a field F , in which case the field of fractions is denoted F .X1 ; : : : ; Xn /, and is also called the field of rational functions (over F , in the variables X1 ; : : : ; Xn ). E XERCISE 16.4. Let F be a field of characteristic zero. Show that F contains an isomorphic copy of Q. E XERCISE 16.5. Show that the field of fractions of ZŒi within C is QŒi . (See Example 7.25 and Exercise 7.13.) 16.3 Unique factorization of polynomials Throughout this section, F denotes a field. Like the ring Z, the ring F ŒX of polynomials is an integral domain, and because of the division with remainder property for polynomials, F ŒX has many other properties in common with Z. Indeed, essentially all the ideas and results from Chapter 1 can be carried over almost verbatim from Z to F ŒX, and in this section, we shall do just that. Recall that the units of F ŒX are precisely the units F of F , that is, the nonzero constants. We call two polynomials g; h 2 F ŒX associate if g D ch for some c 2 F . It is easy to see that g and h are associate if and only if g j h and h j g — indeed, this follows as a special case of part (i) of Theorem 7.4. Clearly, any nonzero polynomial g is associate to a unique monic polynomial (i.e., a polynomial with leading coefficient 1), called the monic associate of g; indeed, the monic associate of g is lc.g/ 1 g (where, as usual, lc.g/ denotes the leading coefficient of g). We call a polynomial f 2 F ŒX irreducible if it is non-constant and all divisors of f are associate to 1 or f . Conversely, we call f reducible if it is non-constant and is not irreducible. Equivalently, a non-constant polynomial f is reducible if and only if there exist polynomials g; h 2 F ŒX of degree strictly less than that of f such that f D gh. Clearly, if g and h are associate polynomials, then g is irreducible if and only if h is irreducible. The irreducible polynomials play a role similar to that of the prime numbers. Just as it is convenient to work with only positive prime numbers, it is also convenient to restrict attention to monic irreducible polynomials. Corresponding to Theorem 1.3, every non-zero polynomial can be expressed as a unit times a product of monic irreducibles in an essentially unique way:
16.3 Unique factorization of polynomials
429
Theorem 16.10. Every non-zero polynomial f 2 F ŒX can be expressed as f D c p1e1 prer ; where c 2 F , p1 ; : : : ; pr are distinct monic irreducible polynomials, and e1 ; : : : ; er are positive integers. Moreover, this expression is unique, up to a reordering of the irreducible polynomials. To prove this theorem, we may assume that f is monic, since the non-monic case trivially reduces to the monic case. The proof of the existence part of Theorem 16.10 is just as for Theorem 1.3. If f is 1 or a monic irreducible, we are done. Otherwise, there exist g; h 2 F ŒX of degree strictly less than that of f such that f D gh, and again, we may assume that g and h are monic. By induction on degree, both g and h can be expressed as a product of monic irreducible polynomials, and hence, so can f . The proof of the uniqueness part of Theorem 16.10 is almost identical to that of Theorem 1.3. The key to the proof is the division with remainder property, Theorem 7.10, from which we can easily derive the following analog of Theorem 1.6: Theorem 16.11. Let I be an ideal of F ŒX. Then there exists a unique polynomial d 2 F ŒX such that I D dF ŒX, where d is either zero or monic. Proof. We first prove the existence part of the theorem. If I D f0g, then d D 0 does the job, so let us assume that I ¤ f0g. Since I contains non-zero polynomials, it must contain monic polynomials, since if g is a non-zero polynomial in I , then its monic associate lc.g/ 1 g is also in I . Let d be a monic polynomial of minimal degree in I . We want to show that I D dF ŒX. We first show that I dF ŒX. To this end, let g be any element in I . It suffices to show that d j g. Using Theorem 7.10, we may write g D dq C r, where deg.r/ < deg.d /. Then by the closure properties of ideals, one sees that r D g dq is also an element of I , and by the minimality of the degree of d , we must have r D 0. Thus, d j g. We next show that dF ŒX I . This follows immediately from the fact that d 2 I and the closure properties of ideals. That proves the existence part of the theorem. As for uniqueness, note that if dF ŒX D eF ŒX, we have d j e and e j d , from which it follows that d and e are associate, and so if d and e are both either monic or zero, they must be equal. For g; h 2 F ŒX, we call d 2 F ŒX a common divisor of g and h if d j g and d j h; moreover, we call such a d a greatest common divisor of g and h if d is monic or zero, and all other common divisors of g and h divide d . Analogous to Theorem 1.7, we have:
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Theorem 16.12. For all g; h 2 F ŒX, there exists a unique greatest common divisor d of g and h, and moreover, gF ŒX C hF ŒX D dF ŒX. Proof. We apply the previous theorem to the ideal I WD gF ŒX C hF ŒX. Let d 2 F ŒX with I D dF ŒX, as in that theorem. Note that g; h; d 2 I and d is monic or zero. It is clear that d is a common divisor of g and h. Moreover, there exist s; t 2 F ŒX such that gs C ht D d . If d 0 j g and d 0 j h, then clearly d 0 j .gs C ht /, and hence d 0 j d . Finally, for uniqueness, if e is a greatest common divisor of g and h, then d j e and e j d , and hence e is associate to d , and the requirement that e is monic or zero implies that e D d . For g; h 2 F ŒX, we denote by gcd.g; h/ the greatest common divisor of g and h. Note that as we have defined it, lc.g/ gcd.g; 0/ D g. Also note that when at least one of g or h are non-zero, gcd.g; h/ is the unique monic polynomial of maximal degree that divides both g and h. An immediate consequence of Theorem 16.12 is that for all g; h 2 F ŒX, there exist s; t 2 F ŒX such that gs C ht D gcd.g; h/, and that when at least one of g or h are non-zero, gcd.g; h/ is the unique monic polynomial of minimal degree that can be expressed as gs C ht for some s; t 2 F ŒX. We say that g; h 2 F ŒX are relatively prime if gcd.g; h/ D 1, which is the same as saying that the only common divisors of g and h are units. It is immediate from Theorem 16.12 that g and h are relatively prime if and only if gF ŒX C hF ŒX D F ŒX, which holds if and only if there exist s; t 2 F ŒX such that gs C ht D 1. Analogous to Theorem 1.9, we have: Theorem 16.13. For f; g; h 2 F ŒX such that f j gh and gcd.f; g/ D 1, we have f j h. Proof. Suppose that f j gh and gcd.f; g/ D 1. Then since gcd.f; g/ D 1, by Theorem 16.12 we have f s C gt D 1 for some s; t 2 F ŒX. Multiplying this equation by h, we obtain f hs C ght D h. Since f j f by definition, and f j gh by hypothesis, it follows that f j h. Analogous to Theorem 1.10, we have: Theorem 16.14. Let p 2 F ŒX be irreducible, and let g; h 2 F ŒX. Then p j gh implies that p j g or p j h. Proof. Assume that p j gh. The only divisors of p are associate to 1 or p. Thus, gcd.p; g/ is either 1 or the monic associate of p. If p j g, we are done; otherwise,
16.3 Unique factorization of polynomials
431
if p − g, we must have gcd.p; g/ D 1, and by the previous theorem, we conclude that p j h. Now to prove the uniqueness part of Theorem 16.10. Suppose we have p1 pr D q1 qs ; where p1 ; : : : ; pr and q1 ; : : : ; qs are monic irreducible polynomials (with duplicates allowed among the pi ’s and among the qj ’s). If r D 0, we must have s D 0 and we are done. Otherwise, as p1 divides the right-hand side, by inductively applying Theorem 16.14, one sees that p1 is equal to qj for some j . We can cancel these terms and proceed inductively (on r). That completes the proof of Theorem 16.10. Analogous to Theorem 1.11, we have: Theorem 16.15. There are infinitely many monic irreducible polynomials in F ŒX. If F is infinite, then this theorem is true simply because there are infinitely many monic, linear polynomials; in any case, one can also just prove this theorem by mimicking the proof of Theorem 1.11 (verify). For a monic irreducible polynomial p, we may define the function p , mapping non-zero polynomials to non-negative integers, as follows: for every polynomial f ¤ 0, if f D p e g, where p − g, then p .f / WD e. We may then write the factorization of f into irreducibles as Y p p .f / ; f Dc p
where the product is over all monic irreducible polynomials p, with all but finitely many of the terms in the product equal to 1. Just as for integers, we may extend the domain of definition of p to include 0, defining p .0/ WD 1. For all polynomials g; h, we have p .g h/ D p .g/ C p .h/ for all p:
(16.1)
From this, it follows that for all polynomials g; h, we have h j g ” p .h/ p .g/ for all p;
(16.2)
p .gcd.g; h// D min.p .g/; p .h// for all p:
(16.3)
and
For g; h 2 F ŒX, a common multiple of g and h is a polynomial m such that g j m and h j m; moreover, such an m is the least common multiple of g and h if m is monic or zero, and m divides all common multiples of g and h. In light
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of Theorem 16.10, it is clear that the least common multiple exists and is unique, and we denote the least common multiple of g and h by lcm.a; b/. Note that as we have defined it, lcm.g; 0/ D 0, and that when both g and h are non-zero, lcm.g; h/ is the unique monic polynomial of minimal degree that is divisible by both g and h. Also, for all g; h 2 F ŒX, we have p .lcm.g; h// D max.p .g/; p .h// for all p:
(16.4)
Just as in §1.3, the notions of greatest common divisor and least common multiple generalize naturally from two to any number of polynomials. We also say that a family of polynomials fgi gkiD1 is pairwise relatively prime if gcd.gi ; gj / D 1 for all indices i; j with i ¤ j . Also just as in §1.3, any rational function g= h 2 F .X/ can be expressed as a fraction g0 = h0 in lowest terms, that is, g= h D g0 = h0 and gcd.g0 ; h0 / D 1, and this representation is unique up to multiplication by units. Many of the exercises in Chapter 1 carry over naturally to polynomials — the reader is encouraged to look over all of the exercises in that chapter, determining which have natural polynomial analogs, and work some of these out. Example 16.11. Let f 2 F ŒX be a polynomial of degree 2 or 3. Then it is easy to see that f is irreducible if and only if f has no roots in F . Indeed, if f is reducible, then it must have a factor of degree 1, which we can assume is monic; thus, we can write f D .X x/g, where x 2 F and g 2 F ŒX, and so f .x/ D .x x/g.x/ D 0. Conversely, if x 2 F is a root of f , then X x divides f (see Theorem 7.12), and so f is reducible. Example 16.12. As a special case of the previous example, consider the polynomials f WD X2 2 2 QŒX and g WD X3 2 2 QŒX. We claim that as polynomials over Q, f and g are irreducible. Indeed, neither of them have integer roots, and so neither of them have rational roots (see Exercise 1.26); therefore, they are irreducible. Example 16.13. In discussing the factorization of polynomials, one must be clear about the coefficient domain. Indeed, if we view f and g in the previous example as polynomials over R, then they factor into irreducibles as p p p p p 3 3 3 f D .X 2/.X C 2/; g D .X 2/.X2 C 2 X C 4/; and over C, g factors even further, as p p p 3 3 g D .X 2/ X 2.1 C i 3/=2 X
p 3
2.1
p i 3/=2 :
16.4 Polynomial congruences
433
P`
E XERCISE 16.6. Suppose f D i D0 ci Xi is an irreducible polynomial over F , P where c0 ¤ 0 and c` ¤ 0. Show that the “reverse” polynomial fQ WD `iD0 c` i Xi is also irreducible. 16.4 Polynomial congruences Throughout this section, F denotes a field. Many of the results from Chapter 2 on congruences modulo a positive integer n carry over almost verbatim to congruences modulo a non-zero polynomial f 2 F ŒX. We state these results here — the proofs of these results are essentially the same as in the integer case, and as such, are omitted for the most part. Because of the division with remainder property for polynomials, we have the analog of Theorem 2.4: Theorem 16.16. Let g; f 2 F ŒX, where f ¤ 0. Then there exists a unique z 2 F ŒX such that z g .mod f / and deg.z/ < deg.f /, namely, z WD g mod f . Corresponding to Theorem 2.5, we have: Theorem 16.17. Let g; f 2 F ŒX with f ¤ 0, and let d WD gcd.g; f /. (i) For every h 2 F ŒX, the congruence gz h .mod f / has a solution z 2 F ŒX if and only if d j h. (ii) For every z 2 F ŒX, we have gz 0 .mod f / if and only if z 0 .mod f =d /. (iii) For all z; z 0 2 F ŒX, we have gz gz 0 .mod f / if and only if z z 0 .mod f =d /. Let g; f 2 F ŒX with f ¤ 0. Part (iii) of Theorem 16.17 gives us a cancellation law for polynomial congruences: if gcd.g; f / D 1 and gz gz 0 .mod f /, then z z 0 .mod f /. We say that z 2 F ŒX is a multiplicative inverse of g modulo f if gz 1 .mod f /. Part (i) of Theorem 16.17 says that g has a multiplicative inverse modulo f if and only if gcd.g; f / D 1. Moreover, part (iii) of Theorem 16.17 says that the multiplicative inverse of g, if it exists, is uniquely determined modulo f. As for integers, we may generalize the “mod” operation as follows. Suppose g; h; f 2 F ŒX, with f ¤ 0, g ¤ 0, and gcd.g; f / D 1. If s is the rational function h=g 2 F .X/, then we define s mod f to be the unique polynomial z 2 F ŒX satisfying gz h .mod f / and deg.z/ < deg.f /:
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With this notation, we can simply write g 1 mod f to denote the unique multiplicative inverse of g modulo f of degree less than deg.f /. Corresponding to Theorem 2.6, we have: Theorem 16.18 (Chinese remainder theorem). Let ffi gkiD1 be a pairwise relatively prime family of non-zero polynomials in F ŒX, and let g1 ; : : : ; gk be arbitrary polynomials in F ŒX. Then there exists a solution g 2 F ŒX to the system of congruences g gi .mod fi / .i D 1; : : : ; k/: Moreover, any g 0 2 F ŒX is a solution to this system of congruences if and only if Q g g 0 .mod f /, where f WD kiD1 fi . Let us recall the formula for the solution g (see proof of Theorem 2.6). We have g WD
k X
gi ei ;
i D1
where ei WD fi ti ; fi WD f =fi ; ti WD .fi /
1
mod fi .i D 1; : : : ; k/:
Now, let us consider the special case of the Chinese remainder theorem where fi D X xi with xi 2 F , and gi D yi 2 F , for i D 1; : : : ; k. The condition that ffi gkiD1 is pairwise relatively prime is equivalent to the condition that the xi ’s are distinct. Observe that a polynomial g 2 F ŒX satisfies the system of congruences g gi .mod fi / .i D 1; : : : ; k/ if and only if Moreover, we have fi get
g.xi / D yi .i D 1; : : : ; k/: Q Q D j ¤i .X xj / and ti D 1= j ¤i .xi
gD
k X i D1
Q
j ¤i .X
xj /
j ¤i .xi
xj /
yi Q
xj / 2 F . So we
:
The reader will recognize this as the usual Lagrange interpolation formula (see Theorem 7.15). Thus, the Chinese remainder theorem for polynomials includes Lagrange interpolation as a special case.
16.4 Polynomial congruences
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Polynomial quotient algebras. Let f 2 F ŒX be a polynomial of degree ` 0, and consider the quotient ring E WD F ŒX=.f /. As discussed in Example 16.7, we may naturally view E as an F -algebra. Moreover, if we set WD ŒXf 2 E, then E D F Œ, and viewing E as a vector space over F , we see that f i 1 g`iD1 is a basis for E. Now suppose ˛ 2 E. We have ˛ D Œgf D g./ for some g 2 F ŒX, and from the above discussion about polynomial congruences, we see that ˛ is a unit if and only if gcd.g; f / D 1. If ` D 0, then E is trivial. If f is irreducible, then E is a field, since g 6 0 .mod f / implies gcd.g; f / D 1. If f is reducible, then E is not a field, and indeed, not even an integral domain: for any non-trivial factor g 2 F ŒX of f , Œgf 2 E is a zero divisor. The Chinese remainder theorem for polynomials also has a more algebraic interpretation. Namely, if ffi gkiD1 is a pairwise relatively prime family of non-zero Q polynomials in F ŒX, and f WD kiD1 fi , then the map W F ŒX=.f / ! F ŒX=.f1 / F ŒX=.fk / Œgf 7! .Œgf1 ; : : : ; Œgfk / is unambiguously defined, and is in fact an F -algebra isomorphism. This map may be seen as a generalization of the ring isomorphism N discussed in Example 7.54. Example 16.14. The polynomial X2 C1 is irreducible over R, since if it were not, it would have a root in R (see Example 16.11), which is clearly impossible, since 1 is not the square of any real number. It follows immediately that C D RŒX=.X2 C1/ is a field, without having to explicitly calculate a formula for the inverse of a nonzero complex number. Example 16.15. Consider the polynomial f WD X4 C X3 C 1 over Z2 . We claim that f is irreducible. It suffices to show that f has no irreducible factors of degree 1 or 2. If f had a factor of degree 1, then it would have a root; however, f .0/ D 0 C 0 C 1 D 1 and f .1/ D 1 C 1 C 1 D 1. So f has no factors of degree 1. Does f have a factor of degree 2? The polynomials of degree 2 are X2 , X2 C X, X2 C 1, and X2 C X C 1. The first and second of these polynomials are divisible by X, and hence not irreducible, while the third has a 1 as a root, and hence is also not irreducible. The last polynomial, X2 C X C 1, has no roots, and hence is the only irreducible polynomial of degree 2 over Z2 . So now we may conclude that if f were not irreducible, it would have to be equal to .X2 C X C 1/2 D X4 C 2X3 C 3X2 C 2X C 1 D X4 C X2 C 1; which it is not.
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Thus, E WD Z2 ŒX=.f / is a field with 24 D 16 elements. We may think of elements E as bit strings of length 4, where the rule for addition is bit-wise “exclusiveor.” The rule for multiplication is more complicated: to multiply two given bit strings, we interpret the bits as coefficients of polynomials (with the left-most bit the coefficient of X3 ), multiply the polynomials, reduce the product modulo f , and write down the bit string corresponding to the reduced product polynomial. For example, to multiply 1001 and 0011, we compute .X3 C 1/.X C 1/ D X4 C X3 C X C 1; and .X4 C X3 C X C 1/ mod .X4 C X3 C 1/ D X: Hence, the product of 1001 and 0011 is 0010. Theorem 7.29 says that E is a cyclic group. Indeed, the element WD 0010 (i.e., D ŒXf ) is a generator for E , as the following table of powers shows: i 1 2 3 4 5 6 7
i 0010 0100 1000 1001 1011 1111 0111
i 8 9 10 11 12 13 14 15
i 1110 0101 1010 1101 0011 0110 1100 0001
Such a table of powers is sometimes useful for computations in small finite fields such as this one. Given ˛; ˇ 2 E , we can compute ˛ˇ by obtaining (by table lookup) i; j such that ˛ D i and ˇ D j , computing k WD .i C j / mod 15, and then obtaining ˛ˇ D k (again by table lookup). 16.5 Minimal polynomials Throughout this section, F denotes a field. Suppose that E is an arbitrary F -algebra, and let ˛ be an element of E. Consider the polynomial evaluation map W F ŒX ! E g 7! g.˛/; which is an F -algebra homomorphism. By definition, the image of is F Œ˛. The kernel of is an ideal of F ŒX, and since every ideal of F ŒX is principal, it follows
16.5 Minimal polynomials
437
that Ker D F ŒX for some polynomial 2 F ŒX; moreover, we can make the choice of unique by insisting that it is monic or zero. The polynomial is called the minimal polynomial of ˛ (over F ). On the one hand, suppose ¤ 0. Since any polynomial that is zero at ˛ is a polynomial multiple of , we see that is the unique monic polynomial of smallest degree that vanishes at ˛. Moreover, the first isomorphism theorems for rings and modules tell us that F Œ˛ is isomorphic (as an F -algebra) to F ŒX=. /, via the isomorphism N W F ŒX=. / ! F Œ˛ Œg 7! g.˛/: Under this isomorphism, ŒX 2 F ŒX=. / corresponds to ˛ 2 F Œ˛, and we see that f˛ i 1 gm m D deg. /. In particular, every i D1 is a basis for F Œ˛ over F , where Pm element of F Œ˛ can be written uniquely as i D1 ci ˛ i 1 , where c1 ; : : : ; cm 2 F . On the other hand, suppose D 0. This means that no non-zero polynomial vanishes at ˛. Also, it means that the map is injective, and hence F Œ˛ is isomorphic (as an F -algebra) to F ŒX; in particular, F Œ˛ is not finitely generated as a vector space over F . Note that if ˛ 2 E has a minimal polynomial ¤ 0, then deg. / > 0, unless E is trivial (i.e., 1E D 0E ), in which case D 1. p p Example 16.16. Consider the real numbers 2 and 3p2. 2 We claim that 2 is the minimal polynomial of 2 over Q. To see p X p this, first observe that 2 is a root of X2 2. Thus, the minimal polynomial of 2 divides 2 2 is irreducible X2 2. However, as we saw in Example 16.12, the polynomial Xp over Q, and hence must be equal to the minimal polynomial of 2 over Q. p 3 3 2 over Q. A similar argument shows that X 2 is the minimal polynomial of p 2 We also see that QŒ 2 is isomorphic (as a Q-algebra) 2/, and p to QŒX=.X since X2 2 is irreducible, it follows that the ring QŒ 2 is actually a field. As a p p vector space over Q, QŒ p 2 has dimension 2, and every element of QŒ 2 may be written uniquely as a C b 2 for a;pb 2 Q. Indeed, for all zero, p a; b 2 Q, not both 2 the multiplicative inverse of a C b 2 is .a=c/ C .b=c/ 2, where c WD a 2b 2 . p 3 Similarly, QŒ 2p is a field and has dimension 3 as a vector space over Q, and p p 3 3 3 every element of QŒ 2 may be written uniquely as a C b 2 C c 4 for a; b; c 2 Q. A simple but important fact is the following: Theorem 16.19. Suppose E is an F -algebra, and that as an F -vector space, E has finite dimension n. Then every ˛ 2 E has a non-zero minimal polynomial of degree at most n.
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Proof. Indeed, the family of elements 1E ; ˛; : : : ; ˛ n must be linearly dependent (as must any family of n C 1 elements of a vector space of dimension n), and hence there exist c0 ; : : : ; cn 2 F , not all zero, such that c0 1E C c1 ˛ C C cn ˛ n D 0E ; P and therefore, the non-zero polynomial f WD i ci Xi vanishes at ˛. Example 16.17. Let f 2 F ŒX be a monic polynomial of degree `, and consider the F -algebra E WD F ŒX=.f / D F Œ, where WD ŒXf 2 E. Clearly, the minimal polynomial of over F is f . Moreover, as a vector space over F , E has dimension `, with f i 1 g`iD1 being a basis. Therefore, every ˛ 2 E has a non-zero minimal polynomial of degree at most `. E XERCISE 16.7. In the field E in Example 16.15, what is the minimal polynomial of 1011 over Z2 ? E XERCISE 16.8. Let W E ! E 0 be an F -algebra homomorphism, let ˛ 2 E, let be the minimal polynomial of ˛ over F , and let 0 be the minimal polynomial of .˛/ over F . Show that 0 j , and that 0 D if is injective. E XERCISE 16.9. Show that if the factorization of f over F ŒX into monic irreducibles is f D f1e1 frer , and if ˛ D Œhf 2 F ŒX=.f /, then the minimal polynomial of ˛ over F is lcm. 1 ; : : : ; r /, where each i is the minimal polynomial of Œhf ei 2 F ŒX=.fiei / over F . i
16.6 General properties of extension fields We now discuss a few general notions related to extension fields. These are all quite simple applications of the theory developed so far. Recall that if F and E are fields, with F being a subring of E, then F is called a subfield of E, and E is called an extension field of F . As usual, we shall blur the distinction between a subring and a natural embedding; that is, if W F ! E is an natural embedding, we shall simply identify elements of F with their images in E under , and in so doing, we may view E as an extension field of F . Usually, the map will be clear from context; for example, if E D F ŒX=.f / for some irreducible polynomial f 2 F ŒX, then we shall simply say that E is an extension field of F , although strictly speaking, F is embedded in E via the map that sends c 2 F to Œcf 2 E.
16.6 General properties of extension fields
439
We start with some definitions. Let E be an extension field of a field F . Then E is an F -algebra via inclusion, and in particular, an F -vector space. If E is a finite dimensional F -vector space, then we say that E is a finite extension of F , and dimF .E/ is called the degree (over F ) of the extension, and is denoted .E W F /; otherwise, we say that E is an infinite extension of F . An element ˛ 2 E is called algebraic over F if there exists a non-zero polynomial g 2 F ŒX such that g.˛/ D 0, and in this case, we define the degree of ˛ (over F ) to be the degree of its minimal polynomial over F (see §16.5); otherwise, ˛ is called transcendental over F . If all elements of E are algebraic over F , then we call E an algebraic extension of F . Suppose E is an extension field of a field F . For ˛ 2 E, we define F .˛/ WD fg.˛/= h.˛/ W g; h 2 F ŒX; h.˛/ ¤ 0g: It is easy to see that F .˛/ is a subfield of E, and indeed, it is the smallest subfield of E containing F and ˛. Clearly, the ring F Œ˛ D fg.˛/ W g 2 F ŒXg, which is the smallest subring of E containing F and ˛, is a subring of F .˛/. We derive some basic properties of F .˛/ and F Œ˛. The analysis naturally breaks down into two cases, depending on whether ˛ is algebraic or transcendental over F . On the one hand, suppose ˛ is algebraic over F . Let be the minimal polynomial of ˛ over F , so that deg. / > 0, and the quotient ring F ŒX=. / is isomorphic (as an F -algebra) to the ring F Œ˛ (see §16.5). Since F Œ˛ is a subring of a field, it must be an integral domain, which implies that F ŒX=. / is an integral domain, and so is irreducible. This in turn implies that F ŒX=. / is a field, and so F Œ˛ is not just a subring of E, it is a subfield of E. Since F Œ˛ is itself already a subfield of E containing F and ˛, it follows that F .˛/ D F Œ˛. Moreover, F Œ˛ is a finite extension of F ; indeed .F Œ˛ W F / D deg. / D the degree of ˛ over F , and the elements 1; ˛; : : : ; ˛ m 1 , where m WD deg. /, form a basis for F Œ˛ over F . On the other hand, suppose that ˛ is transcendental over F . In this case, the minimal polynomial of ˛ over F is the zero polynomial, and the ring F Œ˛ is isomorphic (as an F -algebra) to the ring F ŒX (see §16.5), which is definitely not a field. But consider the “rational function evaluation map” that sends g= h 2 F .X/ to g.˛/= h.˛/ 2 F .˛/. Since no non-zero polynomial over F vanishes at ˛, it is easy to see that this map is well defined, and is in fact an F -algebra isomorphism. Thus, we see that F .˛/ is isomorphic (as an F -algebra) to F .X/. It is also clear that F .˛/ is an infinite extension of F . Let us summarize the above discussion in the following theorem: Theorem 16.20. Let E be an extension field of a field F . (i) If ˛ 2 E is algebraic over F , then F .˛/ D F Œ˛, and F Œ˛ is isomorphic (as an F -algebra) to F ŒX=. /, where is the minimal polynomial of ˛
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over F , which is irreducible; moreover, F Œ˛ is a finite extension of F , and .F Œ˛ W F / D deg. / D the degree of ˛ over F , and the elements 1; ˛; : : : ; ˛ m 1 , where m WD deg. /, form a basis for F Œ˛ over F . (ii) If ˛ 2 E is transcendental over F , then F .˛/ is isomorphic (as an F algebra) to the rational function field F .X/, while the subring F Œ˛ is isomorphic (as an F -algebra) to the ring of polynomials F ŒX; moreover, F .˛/ is an infinite extension of F . Suppose E is an extension field of a field K, which itself is an extension of a field F . Then E is also an extension field of F . The following theorem examines the relation between the degrees of these extensions, in the case where E is a finite extension of K, and K is a finite extension of F . The proof is a simple calculation, which we leave to the reader to verify. Theorem 16.21. Suppose E is a finite extension of a field K, with a basis fˇj gjmD1 over K, and K is a finite extension of F , with a basis f˛i gniD1 over F . Then the elements ˛i ˇj .i D 1; : : : ; nI j D 1; : : : ; m/ form a basis for E over F . In particular, E is a finite extension of F and .E W F / D .E W K/.K W F /: Now suppose that E is a finite extension of a field F . Let K be an intermediate field, that is, a subfield of E containing F . Then evidently, E is a finite extension of K (since any basis for E over F also spans E over K), and K is a finite extension of F (since as F -vector spaces, K is a subspace of E). The previous theorem then implies that .E W F / D .E W K/.K W F /. We have proved: Theorem 16.22. If E is a finite extension of a field F , and K is a subfield of E containing F , then E is a finite extension of K, K is a finite extension of F , and .E W F / D .E W K/.K W F /. Again, suppose that E is a finite extension of a field F . Theorem 16.19 implies that E is algebraic over F , and indeed, that each element of E has degree over F bounded by .E W F /. However, we can say a bit more about these degrees. Suppose ˛ 2 E. Then the degree of ˛ over F is equal to .F Œ˛ W F /, and by the previous theorem, applied to K WD F Œ˛, we have .E W F / D .E W F Œ˛/.F Œ˛ W F /. In particular, the degree of ˛ over F divides .E W F /. We have proved: Theorem 16.23. If E is a finite extension of a field F , then it is an algebraic extension, and for each ˛ 2 E, the degree of ˛ over F divides .E W F /.
16.6 General properties of extension fields
441
p
Example we seepthat the real numbers 2 p 16.18. Continuing with Example 16.16, p and 3 2 are algebraic over Q. The fields QŒ 2 and QŒ 3 2 are extension fields of p p p 3 Q, where .QŒ p2 W Q/ D 2 D the degree of 2 over Q, and .QŒ 2 W Q/ D 3 D the degree of 3 2 over Q. As both of these fields are finite extensions of Q, they are algebraic extensions as well. Since their degrees over Q are prime numbers, it follows that In particular, p they have no subfields other p than themselves and Q.p 3 if ˛ 2 QŒ p2 n Q, then QŒ˛ D QŒ 2. Similarly, if ˛ 2 QŒ 2 n Q, then QŒ˛ D QŒ 3 2. Example 16.19. Continuing with Example 16.17, suppose f 2 F ŒX is a monic irreducible polynomial of degree `, so that E WD F ŒX=.f / D F Œ, where WD ŒXf 2 E, is an extension field of F . The element is algebraic of degree ` over F . Moreover, E is a finite extension of F , with .E W F / D `; in particular, E is an algebraic extension of F , and for each ˛ 2 E, the degree of ˛ over F divides `. As we have seen in Example 16.13, an irreducible polynomial over a field may be reducible when viewed as a polynomial over an extension field. A splitting field is a finite extension of the coefficient field in which a given polynomial splits completely into linear factors. As the next theorem shows, splitting fields always exist. Theorem 16.24. Let F be a field, and f 2 F ŒX a non-zero polynomial of degree n. Then there exists a finite extension E of F over which f factors as f D c.X
˛1 /.X
˛2 / .X
˛n /;
where c 2 F and ˛1 ; : : : ; ˛n 2 E. Proof. We may assume that f is monic. We prove the existence of E by induction on the degree n of f . If n D 0, then the theorem is trivially true. Otherwise, let h be an irreducible factor of f , and set K WD F ŒX=.h/, so that WD ŒXh 2 K is a root of h, and hence of f . So over K, which is a finite extension of F , the polynomial f factors as f D .X
/g;
where g 2 KŒX is a monic polynomial of degree n 1. Applying the induction hypothesis, there exists a finite extension E of K over which g splits into linear factors. Thus, over E, f splits into linear factors, and by Theorem 16.21, E is a finite extension of F . E XERCISE 16.10. In the field E in Example 16.15, find all the elements of degree 2 over Z2 .
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E XERCISE 16.11. Let E be an extension field of a field F , and let ˛1 ; : : : ; ˛n 2 E be algebraic over F . Show that the ring F Œ˛1 ; : : : ; ˛n (see Example 7.45) is in fact a field, and that F Œ˛1 ; : : : ; ˛n is a finite (and hence algebraic) extension of F . p p 3 E XERCISE 16.12. Consider the real numbers 2 and 2. Show that p p p p 3 3 .QŒ 2; 2 W Q/ D .QŒ 2 C 2 W Q/ D 6. p p E XERCISE the real numbers 2 and 3. Show that p p 16.13. Consider p p .QŒ 2; 3 W Q/ D .QŒ 2 C 3 W Q/ D 4. E XERCISE 16.14. Show that if E is an algebraic extension of K, and K is an algebraic extension of F , then E is an algebraic extension of F . E XERCISE 16.15. Let E be an extension of F . Show that the set of all elements of E that are algebraic over F is a subfield of E containing F . E XERCISE 16.16. Consider a field F and its field of rational functions F .X/. Let ˛ 2 F .X/ n F . Show that X is algebraic over F .˛/, and that ˛ is transcendental over F . E XERCISE 16.17. Let E be an extension field of a field F . Suppose ˛ 2 E is transcendental over F , and that E is algebraic over F .˛/. Show that for every ˇ 2 E, ˇ is transcendental over F if and only if E is algebraic over F .ˇ/. 16.7 Formal derivatives Throughout this section, R denotes a ring. Consider a polynomial g 2 RŒX. If Y is another indeterminate, we may evaluate g at X C Y, and collecting monomials of like degree in Y, we may write g X C Y D g0 C g1 Y C g2 Y2 C (16.5) where gi 2 RŒX for i D 0; 1; 2; : : : : Evidently, g0 D g (just substitute 0 for Y in (16.5)), and we may write g X C Y g C g1 Y .mod Y2 /: (16.6) We define the formal derivative of g, denoted D.g/, to be the unique polynomial g1 2 RŒX satisfying (16.6). We stress that unlike the “analytical” notion of derivative from calculus, which is defined in terms of limits, this definition is purely “symbolic.” Nevertheless, some of the usual rules for derivatives still hold: Theorem 16.25. We have: (i) D.c/ D 0 for all c 2 R;
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16.7 Formal derivatives
(ii) D.X/ D 1; (iii) D.g C h/ D D.g/ C D.h/ for all g; h 2 RŒX; (iv) D.gh/ D D.g/h C gD.h/ for all g; h 2 RŒX. Proof. Parts (i) and (ii) are immediate from the definition. Parts (iii) and (iv) follow from the definition by a simple calculation. Suppose g X C Y g C g1 Y .mod Y2 / and h X C Y h C h1 Y .mod Y2 / where g1 D D.g/ and h1 D D.h/. Then .g C h/ X C Y g X C Y C h X C Y .g C h/ C .g1 C h1 /Y .mod Y2 /; and .gh/ X C Y g X C Y h X C Y gh C .g1 h C gh1 /Y .mod Y2 /: Combining parts (i) and (iv) of this theorem, we see that D.cg/ D cD.g/ for all c 2 R and g 2 RŒX. This fact can also be easily derived directly from the definition of the derivative. Combining parts (ii) and (iv) of this theorem, together with a simple induction argument, we see that D.Xn / D nXn 1 for all positive integers n. This fact can also be easily derived directly from the definition of the derivative by considering the binomial expansion of .X C Y/n . Combining part (iii) of this theorem and the observations in the previous two P paragraphs, we see that for any polynomial g D kiD0 ai Xi 2 RŒX, we have D.g/ D
k X
i ai Xi
1
;
(16.7)
i D1
which agrees with the usual formula for the derivative a polynomial. The notion of a formal derivative can be generalized to multi-variate polynomials. Let g 2 RŒX1 ; : : : ; Xn . For any i D 1; : : : ; n, we can view g as a polynomial in the variable Xi , whose coefficients are elements of RŒX1 ; : : : ; Xi 1 ; Xi C1 ; : : : ; Xn . Then if we formally differentiate with respect to the variable Xi , we obtain the formal “partial” derivative DXi .g/. E XERCISE 16.18. Show that for g1 ; : : : ; gn 2 RŒX, we have Y X Y D gi D D.gi / gj i
i
j ¤i
and that for g 2 RŒX, and n 1, we have D.g n / D ng n
1
D.g/:
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E XERCISE 16.19. Prove the “chain rule” for formal derivatives: if g; h 2 RŒX and f D g.h/ 2 RŒX, then D.f / D D.g/.h/ D.h/; more generally, if g 2 RŒX1 ; :::; Xn , and h1 ; :::; hn 2 RŒX, and f D g.h1 ; :::; hn / 2 RŒX, then DX .f / D
n X
DXi .g/.h1 ; :::; hn /DX .hi /:
i D1
E XERCISE 16.20. Let g 2 RŒX, and let x 2 R be a root of g. Show that x is a multiple root of g if and only if x is also a root of D.g/ (see Exercise 7.17). E XERCISE 16.21. Let g 2 RŒX with deg.g/ D k 0, and let x 2 R. Show that if we evaluate g at X C x, writing k X g XCx D bi Xi ; i D0
with b0 ; : : : ; bk 2 R, then we have i Š bi D .Di .g//.x/ for i D 0; : : : ; k. 16.8 Formal power series and Laurent series We discuss generalizations of polynomials that allow an infinite number of nonzero coefficients. Although we are mainly interested in the case where the coefficients come from a field F , we develop the basic theory for general rings R. 16.8.1 Formal power series The ring RŒŒX of formal power series over R consists of all formal expressions of the form g D a0 C a1 X C a2 X2 C ; where a0 ; a1 ; a2 ; : : : 2 R. Unlike ordinary polynomials, we allow an infinite number of non-zero coefficients. We may write such a formal power series as gD
1 X
a i Xi :
i D0
Formally, such a formal power series is an infinite sequence fai g1 i D0 , and the rules for addition and multiplication of are exactly the same as for polynomials. Indeed, the formulas (7.2) and (7.3) in §7.2 for addition and multiplication may be applied directly — all of the relevant sums are finite, and so everything is well defined. We leave it to the reader to verify that with addition and multiplication so defined,
16.8 Formal power series and Laurent series
445
RŒŒX indeed forms a ring. We shall not attempt to interpret a formal power series as a function, and therefore, “convergence” issues shall simply not arise. Clearly, RŒŒX contains RŒX as a subring. Let us consider the group of units of RŒŒX. P i Theorem 16.26. Let g D 1 i D0 ai X 2 RŒŒX. Then g 2 .RŒŒX/ if and only if a0 2 R . Proof. If a0 is not a unit, then it is clear that g is not a unit, since the constant term of a product of formal power series is equal to the product of the constant terms. Conversely, if a0 is a unit, we show how to define the coefficients of the inverse P1 P i i hD 1 i D0 bi X of g. Let f D gh D i D0 ci X . We want f D 1, which means that c0 D 1 and ci D 0 for all i > 0. Now, c0 D a0 b0 , so we set b0 WD a0 1 . Next, we have c1 D a0 b1 C a1 b0 , so we set b1 WD a1 b0 a0 1 . Next, we have c2 D a0 b2 C a1 b1 C a2 b0 , so we set b2 WD .a1 b1 C a2 b0 / a0 1 . Continuing in this way, we see that if we define bi WD .a1 bi 1 C C ai b0 / a0 1 for i 1, then gh D 1. P i Example 16.20. In the ring RŒŒX, the multiplicative inverse of 1 X is 1 i D0 X . E XERCISE 16.22. Let F be a field. Show that every non-zero ideal of F ŒŒX is of the form .Xm / for some uniquely determined integer m 0. 16.8.2 Formal Laurent series One may generalize formal power series to allow a finite number of negative powers of X. The ring R..X// of formal Laurent series over R consists of all formal expressions of the form g D am Xm C amC1 XmC1 C ; where m is allowed to be any integer (possibly negative), and am ; amC1 ; : : : 2 R. Thus, elements of R..X// may have an infinite number of terms involving positive powers of X, but only a finite number of terms involving negative powers of X. We may write such a formal Laurent series as gD
1 X
a i Xi :
i Dm
Formally, such a formal Laurent series is a doubly infinite sequence fai g1 i D 1, with the restriction that for some integer m, we have ai D 0 for all i < m. We may again use the usual formulas (7.2) and (7.3) to define addition and multiplication
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(where the indices i , j , and k now range over all integers, not just the non-negative integers). Note that while the sum in (7.3) has an infinite number of terms, only finitely many of them are non-zero. One may naturally view RŒŒX as a subring of R..X//, and of course, RŒX is a subring of RŒŒX and so also a subring of R..X//. Theorem 16.27. If D is an integral domain, then D..X// is an integral domain. P P i Proof. Let g D 1 a Xi and h D 1 i Dn bi X , where am ¤ 0 and bn ¤ 0. P1 i Dm ii Then gh D i DmCn ci X , where cmCn D am bn ¤ 0. P i Theorem 16.28. Let g 2 R..X//, and suppose that g ¤ 0 and g D 1 i Dm ai X with am 2 R . Then g has a multiplicative inverse in R..X//. Proof. We can write g D Xm g 0 , where g 0 is a formal power series whose constant term is a unit, and hence there is a formal power series h such that g 0 h D 1. Thus, X m h is the multiplicative inverse of g in R..X//. As an immediate corollary, we have: Theorem 16.29. If F is a field, then F ..X// is a field. E XERCISE 16.23. Let F be a field. Show that F ..X// is the field of fractions of F ŒŒX; that is, there is no proper subfield E ¨ F ..X// that contains F ŒŒX. 16.8.3 Reversed Laurent series While formal Laurent series are useful in some situations, in many others, it is more useful and natural to consider reversed Laurent series over R. These are formal expressions of the form gD
m X
ai Xi ;
iD 1
where am ; am 1 ; : : : 2 R. Thus, in a reversed Laurent series, we allow an infinite number of terms involving negative powers of X, but only a finite number of terms involving positive powers of X. Formally, such a reversed Laurent series is a doubly infinite sequence fai g1 i D 1 , with the restriction that for some integer m, we have ai D 0 for all i > m. We may again use the usual formulas (7.2) and (7.3) to define addition and multiplication — and again, the sum in (7.3) has only finitely many non-zero terms. The ring of all reversed Laurent series is denoted R..X 1 //, and as the notation
447
16.8 Formal power series and Laurent series
suggests, the map that sends X to X 1 (and acts as the identity on R) is an Ralgebra isomorphism of R..X// with R..X 1 //. Also, one may naturally view RŒX as a subring of R..X 1 //. P i 1 For g D m i D 1 ai X 2 R..X // with am ¤ 0, let us define the degree of g, denoted deg.g/, to be the value m, and the leading coefficient of g, denoted lc.g/, to be the value am . As for ordinary polynomials, we define the degree of 0 to be 1, and the leading coefficient of 0 to be 0. Note that if g happens to be a polynomial, then these definitions of degree and leading coefficient agree with that for ordinary polynomials. Theorem 16.30. For g; h 2 R..X 1 //, we have deg.gh/ deg.g/ C deg.h/, where equality holds unless both lc.g/ and lc.h/ are zero divisors. Furthermore, if h ¤ 0 and lc.h/ is a unit, then h is a unit, and we have deg.gh 1 / D deg.g/ deg.h/. Proof. Exercise. It is also natural to define a floor function for reversed Laurent series: for g 2 P i R..X 1 // with g D m i D 1 ai X , we define bgc WD
m X
ai Xi 2 RŒXI
i D0
that is, we compute the floor function by simply throwing away all terms involving negative powers of X. Theorem 16.31. Let g; h 2 RŒX with h ¤ 0 and lc.h/ 2 R , and using the usual division with remainder property for polynomials, write g D hq C r, where q; r 2 RŒX with deg.r/ < deg.h/. Let h 1 denote the multiplicative inverse of h in R..X 1 //. Then q D bgh 1 c. Proof. Multiplying the equation g D hq C r by h 1 , we obtain gh and deg.rh 1 / < 0, from which it follows that bgh 1 c D q.
1
D q C rh
1,
Let F be a field, so that F ..X 1 // is also field (this is immediate from Theorem 16.30). Now, F ..X 1 // contains F ŒX as a subring, and hence contains (an isomorphic copy) of the rational function field F .X/. Just as F .X/ corresponds to the field of rational numbers, F ..X 1 // corresponds to the field real numbers. Indeed, we can think of real numbers as decimal numbers with a finite number of digits to the left of the decimal point and an infinite number to the right, and reversed Laurent series have a similar “syntactic” structure. In many ways, this syntactic similarity between the real numbers and reversed Laurent series is more than just superficial.
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E XERCISE 16.24. Write down the rule for determining the multiplicative inverse of an element of R..X 1 // whose leading coefficient is a unit in R. E XERCISE 16.25. Let F be a field of characteristic other than 2. Show that a non-zero g 2 F ..X 1 // has a square-root in F ..X 1 // if and only if deg.g/ is even and lc.g/ has a square-root in F . E XERCISE 16.26. Let R be a ring, and let a 2 R. Show that the multiplicative P inverse of X a in R..X 1 // is j1D1 aj 1 X j . E XERCISE 16.27. Let R be an arbitrary ring, let a1 ; : : : ; a` 2 R, and let f WD .X
a1 /.X
a2 / .X
a` / 2 RŒX:
For j 0, define the “power sum” sj WD
` X
j
ai :
i D1
Show that in the ring R..X
1 //,
we have
` 1 X X 1 D.f / D D sj f .X ai / i D1
1X
j
;
j D1
where D.f / is the formal derivative of f . E XERCISE 16.28. Continuing with the previous exercise, derive Newton’s identities, which state that if f D X` C c1 X` 1 C C c` , with c1 ; : : : ; c` 2 R, then s1 C c1 D 0 s2 C c1 s1 C 2c2 D 0 s3 C c1 s2 C c2 s1 C 3c3 D 0 :: : s` C c1 s` sj C` C c1 sj C`
1
1
C C c`
C C c`
1 s1
1 sj C1
C `c` D 0
C c` sj D 0 .j 1/:
16.9 Unique factorization domains ./ As we have seen, both the ring of integers and the ring of polynomials over a field enjoy a unique factorization property. These are special cases of a more general phenomenon, which we explore here. Throughout this section, D denotes an integral domain.
16.9 Unique factorization domains ./
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We call a; b 2 D associate if a D ub for some u 2 D . Equivalently, a and b are associate if and only if a j b and b j a. A non-zero element p 2 D is called irreducible if it is not a unit, and all divisors of p are associate to 1 or p. Equivalently, a non-zero, non-unit p 2 D is irreducible if and only if it cannot be expressed as p D ab where neither a nor b are units. Definition 16.32. We call D a unique factorization domain (UFD) if (i) every non-zero element of D that is not a unit can be written as a product of irreducibles in D, and (ii) such a factorization into irreducibles is unique up to associates and the order in which the factors appear. Another way to state part (ii) of the above definition is that if p1 pr and are two factorizations of some element as a product of irreducibles, then r D s, and there exists a permutation on the indices f1; : : : ; rg such that pi and 0 p.i are associate. / As we have seen, both Z and F ŒX are UFDs. In both of those cases, we chose to single out a distinguished irreducible element among all those associate to any given irreducible: for Z, we always chose positive primes, and for F ŒX, we chose monic irreducible polynomials. For any specific unique factorization domain D, there may be such a natural choice, but in the general case, there will not be (see Exercise 16.29 below). p10 ps0
Example 16.21. Having already seen two examples of UFDs, it is perhaps a good idea to look pat an example of an integral domain that is not a UFD. Consider the subring ZŒ 3 of p the complex numbers, which consists of all complex numbers of the form a C b 3, where a; b 2 Z. pAs this is a subring of the field C, it is an integral domain (one may also view ZŒ p3 as the quotient ring ZŒX=.X2 C 3/). the units in ZŒ 3. For a; b 2 Z, we have N.a C pLet us first2 determine b 3/pD a C 3b 2 , where N is the usual normpmap on C (see Example 7.5). If 3 is a unit, then there exists ˛ 0 2 ZŒ 3 such that ˛˛ 0 D 1. Taking ˛ 2 ZŒ norms, we obtain 1 D N.1/ D N.˛˛ 0 / D N.˛/N.˛ 0 /: p Since the norm of an elementpof ZŒ 3 is a non-negative integer, this implies that N.˛/ D 1. If ˛ D a C b 3, with a; b 2 Z, then N.˛/ D a2 C 3b 2 , and it is clear p that N.˛/ D 1 if and only if ˛ D ˙1. We conclude that the only units in ZŒ 3 are ˙1. p Now consider the following two factorizations of 4 in ZŒ 3: p p 4 D 2 2 D .1 C 3/.1 3/: (16.8)
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p We claim that 2 is irreducible. For suppose, say, that 2 D ˛˛ 0 , for ˛; ˛ 0 2 ZŒ 3, with neither a unit. Taking norms, we have 4 D N.2/ D N.˛/N.˛ 0 /, and therefore, N.˛/ D N.˛ 0 / D 2 — but this is impossible, since there are no integers a p 2 C 3b 2 D 2. By the same reasoning, since N.1 C and b such that a 3/ D p p p N.1 3/ D 4, we see that 1 C 3 and 1 irreducible. Furp 3 are both p 3 or 1 3, and so the ther, it is clear that 2 is not associate to either 1 C two factorizations of 4 in (16.8) are fundamentally different. For a; b 2 D, we call d 2 D a common divisor of a and b if d j a and d j b; moreover, we call such a d a greatest common divisor of a and b if all other common divisors of a and b divide d . We say that a and b are relatively prime if the only common divisors of a and b are units. It is immediate from the definition of a greatest common divisor that it is unique, up to multiplication by units, if it exists at all. Unlike in the case of Z and F ŒX, in the general setting, greatest common divisors need not exist; moreover, even when they do, we shall not attempt to “normalize” greatest common divisors, and we shall speak only of “a” greatest common divisor, rather than “the” greatest common divisor. Just as for integers and polynomials, we can generalize the notion of a greatest common divisor in an arbitrary integral domain D from two to any number of elements of D, and we can also define a least common multiple of any number of elements as well. Although these greatest common divisors and least common multiples need not exist in an arbitrary integral domain D, if D is a UFD, they will always exist. The existence question easily reduces to the question of the existence of a greatest common divisor and least common multiple of a and b, where a and b are non-zero elements of D. So assuming that D is a UFD, we may write aDu
r Y
piei
and b D v
i D1
r Y
f
pi i ;
i D1
where u and v are units, p1 ; : : : ; pr are non-associate irreducibles, and e1 ; : : : ; er and f1 ; : : : ; fr are non-negative integers, and it is easily seen that r Y
min.ei ;fi /
pi
i D1
is a greatest common divisor of a and b, while r Y
max.ei ;fi /
pi
i D1
is a least common multiple of a and b.
16.9 Unique factorization domains ./
451
It is also evident that in a UFD D, if c j ab and c and a are relatively prime, then c j b. In particular, if p is irreducible and p j ab, then p j a or p j b. This is equivalent to saying that if p is irreducible, then the quotient ring D=pD is an integral domain (and the ideal pD is a prime ideal — see definition above Exercise 7.37). The converse also holds: Theorem 16.33. Suppose D satisfies part (i) of Definition 16.32, and that D=pD is an integral domain for every irreducible p 2 D. Then D is a UFD. Proof. Exercise. E XERCISE 16.29. relation.
(a) Show that the “is associate to” relation is an equivalence
(b) Consider an equivalence class C induced by the “is associate to” relation. Show that if C contains an irreducible element, then all elements of C are irreducible. (c) Suppose that for every equivalence class C that contains irreducibles, we choose one element of C , and call it a distinguished irreducible. Show that D is a UFD if and only if every non-zero element of D can be expressed as up1e1 prer , where u is a unit, p1 ; : : : ; pr are distinguished irreducibles, and this expression is unique up to a reordering of the pi ’s. p E XERCISE 16.30. Show that the ring ZŒ 5 is not a UFD. E XERCISE 16.31. Let D be a UFD and F its field of fractions. Show that (a) every element x 2 F can be expressed as x D a=b, where a; b 2 D are relatively prime, and (b) that if x D a=b for a; b 2 D relatively prime, then for any other a0 ; b 0 2 D with x D a0 =b 0 , we have a0 D ca and b 0 D cb for some c 2 D. E XERCISE 16.32. Let D be a UFD and let p 2 D be irreducible. Show that there is no prime ideal Q of D with f0D g ¨ Q ¨ pD (see definitions above Exercise 7.37). 16.9.1 Unique factorization in Euclidean and principal ideal domains Our proofs of the unique factorization property in both Z and F ŒX hinged on the division with remainder property for these rings. This notion can be generalized, as follows. Definition 16.34. D is said to be a Euclidean domain if there is a “size function”
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S mapping the non-zero elements of D to the set of non-negative integers, such that for all a; b 2 D with b ¤ 0, there exist q; r 2 D, with the property that a D bq C r and either r D 0 or S.r/ < S.b/. Example 16.22. Both Z and F ŒX are Euclidean domains. In Z, we can take the ordinary absolute value function j j as a size function, and for F ŒX, the function deg./ will do. Example 16.23. Recall again the ring ZŒi D fa C bi W a; b 2 Zg of Gaussian integers from Example 7.25. Let us show that this is a Euclidean domain, using the usual norm map N on complex numbers (see Example 7.5) for the size function. Let ˛; ˇ 2 ZŒi , with ˇ ¤ 0. We want to show the existence of ; 2 ZŒi such that ˛ D ˇ C , where N./ < N.ˇ/. Suppose that in the field C, we compute ˛ˇ 1 D r C si , where r; s 2 Q. Let m; n be integers such that jm rj 1=2 and jn sj 1=2 — such integers m and n always exist, but may not be uniquely determined. Set WD m C ni 2 ZŒi and WD ˛ ˇ. Then we have ˛ˇ
1
D C ı;
where ı 2 C with N.ı/ 1=4 C 1=4 D 1=2, and D˛
ˇ D ˛
ˇ.˛ˇ
1
ı/ D ıˇ;
and hence 1 N./ D N.ıˇ/ D N.ı/N.ˇ/ N.ˇ/: 2 Theorem 16.35. If D is a Euclidean domain and I is an ideal of D, then there exists d 2 D such that I D dD. Proof. If I D f0g, then d D 0 does the job, so let us assume that I ¤ f0g. Let d be any non-zero element of I such that S.d / is minimal, where S is a size function that makes D into a Euclidean domain. We claim that I D dD. It will suffice to show that for all c 2 I , we have d j c. Now, we know that there exists q; r 2 D such that c D dq C r, where either r D 0 or S.r/ < S.d /. If r D 0, we are done; otherwise, r is a non-zero element of I with S.r/ < S.d /, contradicting the minimality of S.d /. Recall that an ideal of the form I D dD is called a principal ideal. If all ideals of D are principal, then D is called a principal ideal domain (PID). Theorem 16.35 says that every Euclidean domain is a PID. PIDs enjoy many nice properties, including:
16.9 Unique factorization domains ./
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Theorem 16.36. If D is a PID, then D is a UFD. For the rings Z and F ŒX, the proof of part (i) of Definition 16.32 was a quite straightforward induction argument (as it also would be for any Euclidean domain). For a general PID, however, this requires a different sort of argument. We begin with the following fact: Theorem 16.37. If D is a PID, and I1 I2 is an ascending chain of ideals of D, then there exists an integer k such that Ik D IkC1 D : S Proof. Let I WD 1 ideal of D (see Exercise 7.36). Thus, i D1 Ii , which is an S I D dD for some d 2 D. But d 2 1 i D1 Ii implies that d 2 Ik for some k, which shows that I D dD Ik . It follows that I D Ik D IkC1 D : We can now prove the existence part of Theorem 16.36: Theorem 16.38. If D is a PID, then every non-zero, non-unit element of D can be expressed as a product of irreducibles in D. Proof. Let c 2 D, c ¤ 0, and c not a unit. If c is irreducible, we are done. Otherwise, we can write c D ab, where neither a nor b are units. As ideals, we have cD ¨ aD and cD ¨ bD. If we continue this process recursively, building up a “factorization tree” where c is at the root, a and b are the children of c, and so on, then the recursion must stop, since any infinite path in the tree would give rise to a chain of ideals cD D I1 ¨ I2 ¨ ; contradicting Theorem 16.37. The proof of the uniqueness part of Theorem 16.36 is essentially the same as for proofs we gave for Z and F ŒX. Analogous to Theorems 1.7 and 16.12, we have: Theorem 16.39. Let D be a PID. For all a; b 2 D, there exists a greatest common divisor d of a and b, and moreover, aD C bD D dD. Proof. Exercise. As an immediate consequence of the previous theorem, we see that in a PID D, for all a; b 2 D with greatest common divisor d , there exist s; t 2 D such that as C bt D d ; moreover, a; b 2 D are relatively prime if and only if there exist s; t 2 D such that as C bt D 1. Analogous to Theorems 1.9 and 16.13, we have: Theorem 16.40. Let D be a PID. For all a; b; c 2 D such that c j ab and a and c are relatively prime, we have c j b.
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Proof. Exercise. Analogous to Theorems 1.10 and 16.14, we have: Theorem 16.41. Let D be a PID. Let p 2 D be irreducible, and let a; b 2 D. Then p j ab implies that p j a or p j b. Proof. Exercise. Theorem 16.36 now follows immediately from Theorems 16.38, 16.41, and 16.33. p E XERCISE 16.33. Show that ZΠ2 is a Euclidean domain. E XERCISE 16.34. Consider the polynomial X3
1/.X2 C X C 1/: p p 3/=2. Let ! WD . 1 C 3/=2, and Over C, the roots of X3 1 are 1; p . 1˙ note that ! 2 D 1 ! D . 1 3/=2, and ! 3 D 1. 1 D .X
(a) Show that the ring ZŒ! consists of all elements of the form a C b!, where a; b 2 Z, and is an integral domain. This ring is called the ring of Eisenstein integers. (b) Show that the only units in ZŒ! are ˙1, ˙!, and ˙! 2 . (c) Show that ZŒ! is a Euclidean domain. E XERCISE 16.35. Show that in a PID, all non-zero prime ideals are maximal (see definitions above Exercise 7.37). Recall that for a complex number ˛ D a C bi , with a; b 2 R, the norm of ˛ was defined as N.˛/ D ˛ ˛N D a2 C b 2 (see Example 7.5). There are other measures of the “size” ofpa complexpnumber that are useful. The absolute value of ˛ is defined as j˛j WD N.˛/ D a2 C b 2 . The max norm of ˛ is defined as M.˛/ WD maxfjaj; jbjg. E XERCISE 16.36. Let ˛; ˇ 2 C. Prove the following statements. (a) j˛ˇj D j˛jjˇj. (b) j˛ C ˇj j˛j C jˇj. (c) N.˛ C ˇ/ 2.N.˛/ C N.ˇ//. p (d) M.˛/ j˛j 2M.˛/. The following exercises develop algorithms for computing with Gaussian integers. We shall assume that for computational purposes, a Gaussian integer ˛ D a C bi, with a; b 2 Z, is represented as the pair of integers .a; b/.
16.9 Unique factorization domains ./
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E XERCISE 16.37. Let ˛; ˇ 2 ZŒi . (a) Show how to compute M.˛/ in time O.len.M.˛/// and N.˛/ in time O.len.M.˛//2 /. (b) Show how to compute ˛ C ˇ in time O.len.M.˛// C len.M.ˇ///. (c) Show how to compute ˛ ˇ in time O.len.M.˛// len.M.ˇ///. (d) Assuming ˇ ¤ 0, show how to compute ; 2 ZŒi such that ˛ D ˇ C , N./ 21 N.ˇ/, and N./ 4N.˛/=N.ˇ/. Your algorithm should run in time O.len.M.˛// len.M.ˇ///. Hint: see Example 16.23; also, to achieve the stated running time bound, your algorithm should first test if M.ˇ/ 2M.˛/. E XERCISE 16.38. Using the division with remainder algorithm from part (d) of the previous exercise, adapt the Euclidean algorithm for (ordinary) integers to work with Gaussian integers. On inputs ˛; ˇ 2 ZŒi , your algorithm should compute a greatest common divisor ı 2 ZŒi of ˛ and ˇ in time O.`3 /, where ` WD maxflen.M.˛//; len.M.ˇ//g. E XERCISE 16.39. Extend the algorithm of the previous exercise, so that it computes ; 2 ZŒi such that ˛ Cˇ D ı. Your algorithm should run in time O.`3 /, and it should also be the case that len.M. // and len.M. // are O.`/. The algorithms in the previous two exercises for computing greatest common divisors in ZŒi run in time cubic in the length of their input, whereas the corresponding algorithms for Z run in time quadratic in the length of their input. This is essentially because the running time of the algorithm for division with remainder discussed in Exercise 16.37 is insensitive to the size of the quotient. To get a quadratic-time algorithm for computing greatest common divisors in ZŒi , in the following exercises we shall develop an analog of the binary gcd algorithm for Z. E XERCISE 16.40. Let WD 1 C i 2 ZŒi . (a) Show that 2 D N D ZŒi.
i 2 , that N./ D 2, and that is irreducible in
(b) Let ˛ 2 ZŒi, with ˛ D a C bi for a; b 2 Z. Show that j ˛ if and only if a b is even, in which case ˛ aCb b a D C i: 2 2 (c) Show that for all ˛ 2 ZŒi , we have ˛ 0 .mod / or ˛ 1 .mod /. (d) Show that the quotient ring ZŒi =ZŒi is isomorphic to the ring Z2 .
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(e) Show that for all ˛ 2 ZŒi with ˛ 1 .mod /, there exists a unique 2 f˙1; ˙i g such that ˛ .mod 2/. (f) Show that for all ˛; ˇ 2 ZŒi with ˛ ˇ 1 .mod /, there exists a unique 2 f˙1; ˙i g such that ˛ ˇ .mod 2/. E XERCISE 16.41. We now present a “.1 C i /-ary gcd algorithm” for Gaussian integers. Let WD 1 C i 2 ZŒi . The algorithm takes non-zero ˛; ˇ 2 ZŒi as input, and runs as follows:
./
˛; 0 ˇ; e 0 0 while j and j do =; 0 0 =; e eC1 repeat while j do = while j 0 do 0 0 = if M.0 / < M./ then .; 0 / .0 ; / determine 2 f˙1; ˙i g such that 0 .mod 2/ 0 0 0 until D 0 ı e output ı
Show that this algorithm correctly computes a greatest common divisor of ˛ and ˇ, and can be implemented so as to run in time O.`2 /, where ` WD max.len.M.˛//; len.M.ˇ///. Hint: to analyze the running time, for i D 1; 2; : : : ; let vi (respectively, vi0 ) denote the value of j0 j just before (respectively, after) the execution of the line marked ./ in loop iteration i, and show that p p vi0 .1 C 2/vi and vi C1 vi0 =2 2: E XERCISE 16.42. Extend the algorithm of the previous exercise, so that it computes ; 2 ZŒi such that ˛ Cˇ D ı. Your algorithm should run in time O.`2 /, and it should also be the case that len.M. // and len.M. // are O.`/. Hint: adapt the algorithm in Exercise 4.9. E XERCISE 16.43. In Exercise 16.40, we saw that 2 factors as i.1 C i /2 in ZŒi , where 1Ci is irreducible. This exercise examines the factorization in ZŒi of prime numbers p > 2. Show that: (a) for every irreducible 2 ZŒi , there exists a unique prime number p such that divides p; (b) for all prime numbers p 1 .mod 4/, we have p D , N where 2 ZŒi is irreducible, and the complex conjugate N of is also irreducible and not associate to ;
16.9 Unique factorization domains ./
457
(c) all prime numbers p 3 .mod 4/ are irreducible in ZŒi . Hint: for parts (b) and (c), use Theorem 2.34. 16.9.2 Unique factorization in DŒX In this section, we prove the following: Theorem 16.42. If D is a UFD, then so is DŒX. This theorem implies, for example, that ZŒX is a UFD. Applying the theorem inductively, one also sees that ZŒX1 ; : : : ; Xn is a UFD, as is F ŒX1 ; : : : ; Xn for every field F . We begin with some simple observations. First, recall that for an integral domain D, DŒX is an integral domain, and the units in DŒX are precisely the units in D. Second, it is easy to see that an element of D is irreducible in D if and only if it is P irreducible in DŒX. Third, for c 2 D and f D i ci Xi 2 DŒX, we have c j f if and only if c j ci for all i . We call a non-zero polynomial f 2 DŒX primitive if the only elements of D that divide f are units. If D is a UFD, then given any non-zero polynomial f 2 DŒX, we can write it as f D cf 0 , where c 2 D and f 0 2 DŒX is a primitive polynomial: just take c to be a greatest common divisor of all the coefficients of f. Example 16.24. In ZŒX, the polynomial f D 4X2 C 6X C 20 is not primitive, but we can write f D 2f 0 , where f 0 D 2X2 C 3X C 10 is primitive. It is easy to prove the existence part of Theorem 16.42: Theorem 16.43. Let D be a UFD. Every non-zero, non-unit element of DŒX can be expressed as a product of irreducibles in DŒX. Proof. Let f be a non-zero, non-unit polynomial in DŒX. If f is a constant, then because D is a UFD, f factors into irreducibles in D. So assume f is not constant. If f is not primitive, we can write f D cf 0 , where c is a non-zero, non-unit in D, and f 0 is a primitive, non-constant polynomial in DŒX. Again, as D is a UFD, c factors into irreducibles in D. From the above discussion, it suffices to prove the theorem for non-constant, primitive polynomials f 2 DŒX. If f is itself irreducible, we are done. Otherwise, we can write f D gh, where g; h 2 DŒX and neither g nor h are units. Further, by the assumption that f is a primitive, non-constant polynomial, both g and h must also be primitive, non-constant polynomials; in particular, both g and h have degree strictly less than deg.f /, and the theorem follows by induction on degree.
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The uniqueness part of Theorem 16.42 is (as usual) more difficult. We begin with the following fact: Theorem 16.44. Let D be a UFD, let p be an irreducible in D, and let g; h 2 DŒX. Then p j gh implies p j g or p j h. Proof. Consider the quotient ring D=pD, which is an integral domain (because D is a UFD), and the corresponding ring of polynomials .D=pD/ŒX, which is also an integral domain. Also consider the natural map that sends a 2 D to aN WD Œap 2 D=pD, which we can extend coefficient-wise to a ring homomorphism from DŒX to .D=pD/ŒX (see Example 7.46). If p j gh, then we have N 0 D gh D gN h; and since .D=pD/ŒX is an integral domain, it follows that gN D 0 or hN D 0, which means that p j g or p j h. Theorem 16.45. Let D be a UFD. The product of two primitive polynomials in DŒX is also primitive. Proof. Let g; h 2 DŒX be primitive polynomials, and let f WD gh. If f is not primitive, then c j f for some non-zero, non-unit c 2 D, and as D is a UFD, there is some irreducible element p 2 D that divides c, and therefore, divides f as well. By Theorem 16.44, it follows that p j g or p j h, which implies that either g is not primitive or h is not primitive. Suppose that D is a UFD and that F is its field of fractions. Any non-zero polynomial f 2 F ŒX can always be written as f D .c=d /f 0 , where c; d 2 D, with d ¤ 0, and f 0 2 DŒX is primitive. To see this, clear the denominators of the coefficients of f , writing df D f 00 , where 0 ¤ d 2 D and f 00 2 DŒX. Then take c to be a greatest common divisor of the coefficients of f 00 , so that f 00 D cf 0 , where f 0 2 DŒX is primitive. Then we have f D .c=d /f 0 , as required. Of course, we may assume that c and d are relatively prime — if not, we may divide c and d by a greatest common divisor. Example 16.25. Let f D .3=5/X2 C 9X C 3=2 2 QŒX. Then we can write f D .3=10/f 0 , where f 0 D 2X2 C 30X C 5 2 ZŒX is primitive. As a consequence of the previous theorem, we have: Theorem 16.46. Let D be a UFD and let F be its field of fractions. Let f; g 2 DŒX and h 2 F ŒX be non-zero polynomials such that f D gh and g is primitive. Then h 2 DŒX. Proof. Write h D .c=d /h0 , where c; d 2 D and h0 2 DŒX is primitive. Let us
16.9 Unique factorization domains ./
459
assume that c and d are relatively prime. Then we have d f D c gh0 :
(16.9)
We claim that d 2 D . To see this, note that (16.9) implies that d j .c gh0 /, and the assumption that c and d are relatively prime implies that d j gh0 . But by Theorem 16.45, gh0 is primitive, from which it follows that d is a unit. That proves the claim. It follows that c=d 2 D, and hence h D .c=d /h0 2 DŒX. Theorem 16.47. Let D be a UFD and F its field of fractions. If f 2 DŒX with deg.f / > 0 is irreducible, then f is also irreducible in F ŒX. Proof. Suppose that f is not irreducible in F ŒX, so that f D gh for non-constant polynomials g; h 2 F ŒX, both of degree strictly less than that of f . We may write g D .c=d /g 0 , where c; d 2 D and g 0 2 DŒX is primitive. Set h0 WD .c=d /h, so that f D gh D g 0 h0 . By Theorem 16.46, we have h0 2 DŒX, and this shows that f is not irreducible in DŒX. Theorem 16.48. Let D be a UFD. Let f 2 DŒX with deg.f / > 0 be irreducible, and let g; h 2 DŒX. If f divides gh in DŒX, then f divides either g or h in DŒX. Proof. Suppose that f 2 DŒX with deg.f / > 0 is irreducible. This implies that f is a primitive polynomial. By Theorem 16.47, f is irreducible in F ŒX, where F is the field of fractions of D. Suppose f divides gh in DŒX. Then because F ŒX is a UFD, f divides either g or h in F ŒX. But Theorem 16.46 implies that f divides either g or h in DŒX. Theorem 16.42 now follows immediately from Theorems 16.43, 16.44, and 16.48, together with Theorem 16.33. In the proof of Theorem 16.42, there is a clear connection between factorization in DŒX and F ŒX, where F is the field of fractions of D. We should perhaps make this connection more explicit. Let f 2 DŒX be a non-zero polynomial. We may write f as f D up1a1 prar f1b1 fsbs : where u 2 D , the pi ’s are non-associate, irreducible constants, and the fj ’s are non-associate, irreducible, non-constant polynomials (and in particular, primitive). For j D 1; : : : ; s, let gj WD lc.fj / 1 fj be the monic associate of fj in F ŒX. Then in F ŒX, f factors as f D cg1b1 gsbs ;
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where c WD u
Y
piai
i
Y
lc.fj /bj 2 F;
j
and the gj ’s are distinct, irreducible, monic polynomials in F ŒX. Example 16.26. Consider the polynomial f D 4X2 C 2X 2 2 ZŒX. Over ZŒX, f factors as 2.2X 1/.X C 1/, where each of these three factors is irreducible in ZŒX. However, over QŒX, f factors as 4.X 1=2/.X C 1/, where 4 is a unit, and the other two factors are irreducible. The following theorem provides a useful criterion for establishing that a polynomial is irreducible. Theorem 16.49 (Eisenstein’s criterion). Let D be a UFD and F its field of fractions. Let f D cn Xn C cn 1 Xn 1 C C c0 2 DŒX. If there exists an irreducible p 2 D such that p − cn ; p j cn
1;
; p j c0 ; p 2 − c0 ;
then f is irreducible over F . Proof. Let f be as above, and suppose it were not irreducible in F ŒX. Then by Theorem 16.47, we could write f D gh, where g; h 2 DŒX, both of degree strictly less than that of f . Let us write g D ak Xk C C a0 and h D b` X` C C b0 ; where ak ¤ 0 and b` ¤ 0, so that 0 < k < n and 0 < ` < n. Now, since cn D ak b` , and p − cn , it follows that p − ak and p − b` . Further, since c0 D a0 b0 , and p j c0 but p 2 − c0 , it follows that p divides one of a0 or b0 , but not both — for concreteness, let us assume that p j a0 but p − b0 . Also, let m be the smallest positive integer such that p − am — note that 0 < m k < n. Now consider the natural map that sends a 2 D to aN WD Œap 2 D=pD, which we can extend coefficient-wise to a ring homomorphism from DŒX to .D=pD/ŒX (see Example 7.46). Because D is a UFD and p is irreducible, D=pD is an integral domain. Since f D gh, we have cNn Xn D fN D gN hN D .aN k Xk C C aN m Xm /.bN` X` C C bN0 /:
(16.10)
But notice that when we multiply out the two polynomials on the right-hand side of (16.10), the coefficient of Xm is aN m bN0 ¤ 0, and as m < n, this clearly contradicts the fact that the coefficient of Xm in the polynomial on the left-hand side of (16.10) is zero. As an application of Eisenstein’s criterion, we have:
16.9 Unique factorization domains ./
461
Theorem 16.50. For every prime number q, the qth cyclotomic polynomial ˆq WD
Xq 1 D Xq X 1
1
C Xq
2
C C 1
is irreducible over Q. Proof. Let .X C 1/q 1 f WD ˆq X C 1 D : .X C 1/ 1 It is easy to see that f D
q X1 i D0
q ci X ; where ci D i C1 i
! .i D 0; : : : ; q
1/:
Thus, cq 1 D 1, c0 D q, and for 0 < i < q 1, we have q j ci (see Exercise 1.14). Theorem 16.49 therefore applies, and we conclude that f is irreducible over Q. It follows that ˆq is irreducible over Q, since if ˆq D gh were a non-trivial factorization of ˆq , then f D ˆq X C 1 D g X C 1 h X C 1 would be a non-trivial factorization of f . E XERCISE 16.44. Show that neither ZŒX nor F ŒX; Y (where F is a field) are PIDs (even though they are UFDs). E XERCISE 16.45. Let f 2 ZŒX be a monic polynomial. Show that if f has a root x 2 Q, then x 2 Z, and x divides the constant term of f . E XERCISE 16.46. Let D be a UFD, let p be an irreducible element of D, and consider the natural map that sends a 2 D to aN WD Œap 2 D=pD, which we extend coefficient-wise to a ring homomorphism from DŒX to .D=pD/ŒX (see Example 7.46). Show that if f 2 DŒX is a primitive polynomial such that p − lc.f / and fN 2 .D=pD/ŒX is irreducible, then f is irreducible. E XERCISE 16.47. Let a be a non-zero, square-free integer, with a … f˙1g, and let n be a positive integer. Show that the polynomial Xn a is irreducible in QŒX. E XERCISE 16.48. Show that the polynomial X4 C 1 is irreducible in QŒX. E XERCISE 16.49. Let F be a field, and consider the ring of bivariate polynomials F ŒX; Y. Show that in this ring, the polynomial X2 C Y2 1 is irreducible, provided F does not have characteristic 2. What happens if F has characteristic 2? E XERCISE 16.50. Design and analyze an efficient algorithm for the following problem. The input is a pair of polynomials g; h 2 ZŒX, along with their greatest
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More rings
common divisor d in the ring QŒX. The output is the greatest common divisor of g and h the ring ZŒX. E XERCISE 16.51. Let g; h 2 ZŒX be non-zero polynomials with d WD gcd.g; h/ 2 ZŒX. Show that for every prime p not dividing lc.g/ lc.h/, we have N and except for finitely many primes p, we have dN D gcd.g; N dN j gcd.g; N h/, N h/. Here, dN , g, N and hN denote the images of d , g, and h in Zp ŒX under the coefficientwise extension of the natural map from Z to Zp (see Example 7.47). E XERCISE 16.52. Let F be a field, and let g; h 2 F ŒX; Y. Define V .g; h/ WD f.x; y/ 2 F F W g.x; y/ D h.x; y/ D 0g. Show that if g and h are relatively prime, then V .g; h/ is a finite set. Hint: consider the rings F .X/ŒY and F .Y/ŒX. 16.10 Notes The “.1 C i/-ary gcd algorithm” in Exercise 16.41 for computing greatest common divisors of Gaussian integers is based on algorithms in Weilert [104] and Damgård and Frandsen [31]. The latter paper also develops a corresponding algorithm for Eisenstein integers (see Exercise 16.34). Weilert [105] presents an asymptotically fast algorithm that computes the greatest common divisor of Gaussian integers of length at most ` in time O.`1Co.1/ /.
17 Polynomial arithmetic and applications
In this chapter, we study algorithms for performing arithmetic on polynomials. Initially, we shall adopt a very general point of view, discussing polynomials whose coefficients lie in an arbitrary ring R, and then specialize to the case where the coefficient ring is a field F . There are many similarities between arithmetic in Z and in RŒX, and the similarities between Z and F ŒX run even deeper. Many of the algorithms we discuss in this chapter are quite similar to the corresponding algorithms for integers. As we did in Chapter 14 for matrices, we shall treat R as an “abstract data type,” and measure the complexity of algorithms for polynomials over a ring R by counting “operations in R.” 17.1 Basic arithmetic Throughout this section, R denotes a non-trivial ring. For computational purposes, we shall assume that a polynomial g D Pk 1 i i D0 ai X 2 RŒX is represented as a coefficient vector .a0 ; a1 ; : : : ; ak 1 /. Further, when g is non-zero, the coefficient ak 1 should be non-zero. The basic algorithms for addition, subtraction, multiplication, and division of polynomials are quite straightforward adaptations of the corresponding algorithms for integers. In fact, because of the lack of “carries,” these algorithms are actually much simpler in the polynomial case. We briefly discuss these algorithms here — analogous to our treatment of integer arithmetic, we do not discuss the details of “stripping” leading zero coefficients. For addition and subtraction, all we need to do is to add or subtract coefficient vectors. P P For multiplication, let g D ikD01 ai Xi 2 RŒX and h D `i D01 bi Xi 2 RŒX, P 2 where k 1 and ` 1. The product f WD a b is of the form f D kC` ci Xi , i D0 the coefficients of which can be computed using O.k`/ operations in R as follows: 463
464
Polynomial arithmetic and applications
for i for i
0 to k C ` 2 do ci 0 0 to k 1 do for j 0 to ` 1 do ci Cj ci Cj C ai bj
P P For division, let g D ki D01 ai Xi 2 RŒX and h D `i D01 bi Xi 2 RŒX, where b` 1 2 R . We want to compute polynomials q; r 2 RŒX such that g D hq C r, where deg.r/ < ` 1. If k < `, we can simply set q 0 and r g; otherwise, we can compute q and r using O.`.k `C1// operations in R using the following algorithm: t b` 11 2 R for i 0 to k 1 do ri ai for i k ` down to 0 do qi t ri C` 1 for j 0 to ` 1 do ri Cj ri Cj qi bj P` 2 Pk ` i i q i D0 qi X , r i D0 ri X With these simple algorithms, we obtain the polynomial analog of Theorem 3.3. Let us define the length of g 2 RŒX, denoted len.g/, to be the length of its coefficient vector; more precisely, we define deg.g/ C 1 if g ¤ 0, len.g/ WD 1 if g D 0. It is sometimes more convenient to state the running times of algorithms in terms of len.g/, rather than deg.g/ (the latter has the inconvenient habit of taking on the value 0, or worse, 1). Theorem 17.1. Let g and h be arbitrary polynomials in RŒX. (i) We can compute g ˙ h with O.len.g/ C len.h// operations in R. (ii) We can compute g h with O.len.g/ len.h// operations in R. (iii) If h ¤ 0 and lc.h/ 2 R , we can compute q; r 2 RŒX such that g D hq Cr and deg.r/ < deg.h/ with O.len.h/ len.q// operations in R. Analogous to algorithms for modular integer arithmetic, we can also do arithmetic in the residue class ring RŒX=.f /, where f 2 RŒX is a polynomial of degree ` > 0 with lc.f / 2 R . For each ˛ 2 RŒX=.f /, there exists a unique polynomial g 2 RŒX with deg.g/ < ` and ˛ D Œgf ; we call this polynomial g the canonical representative of ˛, and denote it by rep.˛/. For computational purposes, we represent elements of RŒX=.f / by their canonical representatives.
17.1 Basic arithmetic
465
With this representation, addition and subtraction in RŒX=.f / can be performed using O.`/ operations in R, while multiplication takes O.`2 / operations in R. The repeated-squaring algorithm for computing powers works equally well in this setting: given ˛ 2 RŒX=.f / and a non-negative exponent e, we can compute ˛ e using O.len.e// multiplications in RŒX=.f /, for a total of O.len.e/ `2 / operations in R. The following exercises deal with arithmetic with polynomials RŒX over a ring R. E XERCISE 17.1. State and re-work the polynomial analogs of Exercises 3.26– 3.28. E XERCISE 17.2. Given a polynomial g 2 RŒX and an element x 2 R, a particularly elegant and efficient way of computing g.x/ is called Horner’s rule. Suppose P g D ki D01 ai Xi , where k 0 and ai 2 R for i D 0; : : : ; k 1. Horner’s rule computes g.x/ as follows: y 0R for i k 1 down to 0 do y yx C ai output y Show that this algorithm correctly computes g.x/ using k multiplications in R and k additions in R. E XERCISE 17.3. Let f 2 RŒX be a polynomial of degree ` > 0 with lc.f / 2 R , and let E WD RŒX=.f /. Suppose that in addition to f , we are given a polynomial g 2 RŒX of degree less than k and an element ˛ 2 E, and we want to compute g.˛/ 2 E. (a) Show that a straightforward application of Horner’s rule yields an algorithm that uses O.k`2 / operations in R, and requires space for storing O.`/ elements of R. (b) Show how to compute g.˛/ using just O.k` C k 1=2 `2 / operations in R, at the expense of requiring space for storing O.k 1=2 `/ elements of R. Hint: first compute a table of powers 1; ˛; : : : ; ˛ m , for m k 1=2 . E XERCISE 17.4. Given polynomials g; h 2 RŒX, show how to compute their composition g.h/ 2 RŒX using O.len.g/2 len.h/2 / operations in R. E XERCISE 17.5. Suppose you are given three polynomials f; g; h 2 Zp ŒX, where p is a large prime, in particular, p 2 deg.g/ deg.h/. Design an efficient probabilistic algorithm that tests if f D g.h/ (i.e., if f equals g composed
466
Polynomial arithmetic and applications
with h). Your algorithm should have the following properties: if f D g.h/, it should always output “true,” and otherwise, it should output “false” with probability at least 0:999. The expected running time of your algorithm should be O..len.f / C len.g/ C len.h// len.p/2 /. E XERCISE 17.6. Let x; a0 ; : : : ; a` 1 2 R, and let k be an integer with 0 k `. P 1 For i D 0; : : : ; ` k, define gi WD ji Ck aj Xj 2 RŒX. Show how to compute Di the ` k C 1 values g0 .x/; : : : ; g` k .x/ using O.`/ operations in R. 17.2 Computing minimal polynomials in F ŒX=.f / (I) In this section, we shall examine a computational problem to which we shall return on several occasions, as it will serve to illustrate a number of interesting algebraic and algorithmic concepts. Let F be a field, f 2 F ŒX a monic polynomial of degree ` > 0. Also, let E WD F ŒX=.f /, which is an F -algebra, and in particular, an F -vector space. As an F -vector space, E has dimension `. Suppose we are given an element ˛ 2 E, and want to efficiently compute the minimal polynomial of ˛ over F , that is, the monic polynomial 2 F ŒX of least degree such that .˛/ D 0, which we know has degree at most ` (see §16.5). We can solve this problem using polynomial arithmetic and Gaussian elimination, as follows. Consider the F -linear map W F ŒX` ! E that sends a polynomial g 2 F ŒX of degree at most ` to g.˛/. To perform the linear algebra, we need to specify bases for F ŒX` and E. For F ŒX` , let us work with the basis P` i S WD fX`C1 i g`C1 i D0 ai X 2 F ŒX` , the i D1 . With this choice of basis, for g D 1.`C1/ coordinate vector of g is VecS .g/ D .a` ; : : : ; a0 / 2 F . For E, let us work ` i 1 with the basis T WD f gi D1 . Let A WD MatS;T ./ 2 F .`C1/` I that is, A is the matrix of relative to S and T (see §14.2). For i D 1; : : : ; ` C 1, the ith row of A is the coordinate vector VecT .˛ `C1 i / 2 F 1` . We apply Gaussian elimination to A to find row vectors v1 ; : : : ; vs 2 F 1.`C1/ that are coordinate vectors corresponding to a basis for the kernel of . Now, the coordinate vector of the minimal polynomial of ˛ is a linear combination of v1 ; : : : ; vs . To find it, we form the s .` C 1/ matrix B whose rows consist of v1 ; : : : ; vs , and apply Gaussian elimination to B, obtaining an s .` C 1/ matrix B 0 in reduced row echelon form whose row space is the same as that of B. Let be the polynomial whose coordinate vector is the last row of B 0 . Because of the choice
467
17.3 Euclid’s algorithm
of basis for F ŒX` , and because B 0 is in reduced row echelon form, it is clear that no non-zero polynomial in Ker has degree less than that of . Moreover, as is already monic (again, by the fact that B 0 is in reduced row echelon form), it follows that is in fact the minimal polynomial of ˛ over F . The total amount of work performed by this algorithm is O.`3 / operations in F to build the matrix A (this just amounts to computing ` successive powers of ˛, that is, O.`/ multiplications in E, each of which takes O.`2 / operations in F ), and O.`3 / operations in F to perform both Gaussian elimination steps. 17.3 Euclid’s algorithm In this section, F denotes a field, and we consider the computation of greatest common divisors in F ŒX. The basic Euclidean algorithm for integers is easily adapted to compute gcd.g; h/, for polynomials g; h 2 F ŒX. Analogous to the integer case, we assume that deg.g/ deg.h/; however, we shall also assume that g ¤ 0. This is not a serious restriction, of course, as gcd.0; 0/ D 0, and making this restriction will simplify the presentation a bit. Recall that we defined gcd.g; h/ to be either zero or monic, and the assumption that g ¤ 0 means that gcd.g; h/ is non-zero, and hence monic. The following is the analog of Theorem 4.1, and is based on the division with remainder property for polynomials. Theorem 17.2. Let g; h 2 F ŒX, with deg.g/ deg.h/ and g ¤ 0. Define the polynomials r0 ; r1 ; : : : ; r`C1 2 F ŒX, and q1 ; : : : ; q` 2 F ŒX, where ` 0, as follows: g D r0 ; h D r1 ; r0 D r1 q1 C r2 :: :
.0 deg.r2 / < deg.r1 //;
D ri qi C ri C1 :: :
ri
1
r`
2
D r`
r`
1
D r` q`
1 q` 1
.0 deg.ri C1 / < deg.ri //;
C r`
.0 deg.r` / < deg.r`
1 //;
.r`C1 D 0/:
Note that by definition, ` D 0 if h D 0, and ` > 0 otherwise. Then we have r` = lc.r` / D gcd.g; h/, and if h ¤ 0, then ` deg.h/ C 1.
468
Polynomial arithmetic and applications
Proof. Arguing as in the proof of Theorem 4.1, one sees that gcd.g; h/ D gcd.r0 ; r1 / D gcd.r` ; r`C1 / D gcd.r` ; 0/ D r` = lc.r` /: That proves the first statement. For the second statement, if h ¤ 0, then the degree sequence deg.r1 /; deg.r2 /; : : : ; deg.r` / is strictly decreasing, with deg.r` / 0, from which it follows that deg.h/ D deg.r1 / ` 1. This gives us the following polynomial version of the Euclidean algorithm: Euclid’s algorithm. On input g; h, where g; h 2 F ŒX with deg.g/ deg.h/ and h ¤ 0, compute d D gcd.g; h/ as follows: r g; r 0 h while r 0 ¤ 0 do r 00 r mod r 0 0 .r; r / .r 0 ; r 00 / d r= lc.r/ // make monic output d Theorem 17.3. Euclid’s algorithm for polynomials uses O.len.g/ len.h// operations in F . Proof. The proof is almost identical to that of Theorem 4.2. Details are left to the reader. Just as for integers, if d D gcd.g; h/, then gF ŒXChF ŒX D dF ŒX, and so there exist polynomials s and t such that gs C ht D d . The procedure to calculate s and t is precisely the same as in the integer case; however, in the polynomial case, we can be much more precise about the relative sizes of the objects involved in the calculation. Theorem 17.4. Let g, h, r0 ; : : : ; r`C1 and q1 ; : : : ; q` be as in Theorem 17.2. Define polynomials s0 ; : : : ; s`C1 2 F ŒX and t0 ; : : : ; t`C1 2 F ŒX as follows: s0 WD 1; t0 WD 0; s1 WD 0; t1 WD 1; and for i D 1; : : : ; `, si C1 WD si Then:
1
si qi ; ti C1 WD ti
1
ti qi :
469
17.3 Euclid’s algorithm
(i) for i D 0; : : : ; ` C 1, we have gsi C hti D ri ; in particular, gs` C ht` D lc.r` / gcd.g; h/; ti si C1 D . 1/i ;
(ii) for i D 0; : : : ; `, we have si ti C1
(iii) for i D 0; : : : ; ` C 1, we have gcd.si ; ti / D 1; (iv) for i D 1; : : : ; ` C 1, we have deg.ti / D deg.g/
deg.ri
1 /;
deg.ri
1 /I
and for i D 2; : : : ; ` C 1, we have deg.si / D deg.h/
(v) for i D 1; : : : ; ` C 1, we have deg.ti / deg.g/ and deg.si / deg.h/; if deg.g/ > 0 and h ¤ 0, then deg.t` / < deg.g/ and deg.s` / < deg.h/. Proof. (i), (ii), and (iii) are proved just as in the corresponding parts of Theorem 4.3. For (iv), the proof will hinge on the following facts: For i D 1; : : : ; `, we have deg.ri 1 / deg.ri /, and since qi is the quotient in dividing ri 1 by ri , we have deg.qi / D deg.ri 1 / deg.ri /. For i D 2; : : : ; `, we have deg.ri
1/
> deg.ri /.
We prove the statement involving the ti ’s by induction on i , and leave the proof of the statement involving the si ’s to the reader. One can see by inspection that this statement holds for i D 1, since deg.t1 / D 0 and r0 D g. If ` D 0, there is nothing more to prove, so assume that ` > 0 and h ¤ 0. Now, for i D 2, we have t2 D 0 1 q1 D q1 . Thus, deg.t2 / D deg.q1 / D deg.r0 / deg.r1 / D deg.g/ deg.r1 /. Now for the induction step. Assume i 3. Then we have deg.ti
1 qi 1 /
D deg.ti
1/
C deg.qi
D deg.g/
deg.ri
2/
D deg.g/
deg.ri
1/
1/
(since deg.qi > deg.g/ D deg.ti By definition, ti D ti
2
ti
deg.ri 2/
1/
C deg.qi
D deg.ri
3/
1 qi 1 ,
deg.ri 3/
1 /)
> deg.ri
1 /)
and from the above reasoning, we see that
from which it follows that deg.ti / D deg.g/
deg.ri
1/
(by induction)
(by induction).
1 qi 1 /
deg.ri
2/
(since deg.ri
D deg.ti
deg.g/
1/
> deg.ti 1 /.
2 /;
470
Polynomial arithmetic and applications
(v) follows easily from (iv). From this theorem, we obtain the following algorithm: The extended Euclidean algorithm. On input g; h, where g; h 2 F ŒX with deg.g/ deg.h/ and g ¤ 0, compute d , s, and t , where d; s; t 2 F ŒX, d D gcd.g; h/ and gs C ht D d , as follows: r g; r 0 h s 1; s 0 0 t 0; t 0 1 while r 0 ¤ 0 do compute q; r 00 such that r D r 0 q C r 00 , with deg.r 00 / < deg.r 0 / .r; s; t; r 0 ; s 0 ; t 0 / .r 0 ; s 0 ; t 0 ; r 00 ; s s 0 q; t t 0 q/ c lc.r/ d r=c; s s=c; t t =c // make monic output d; s; t Theorem 17.5. The extended Euclidean algorithm for polynomials uses O.len.g/ len.h// operations in F . Proof. Exercise. E XERCISE 17.7. Let g; h 2 F ŒX, with deg.g/ deg.h/ 0, let d WD gcd.g; h/, and let g 0 WD g=d . Show that Euclid’s algorithm on input g; h uses O.len.g 0 / len.h// operations in F . 17.4 Computing modular inverses and Chinese remaindering In this and the remaining sections of this chapter, we explore various applications of Euclid’s algorithm for polynomials. Most of these applications are analogous to their integer counterparts, although there are some differences to watch for. Throughout this section, F denotes a field. We begin with the obvious application of the extended Euclidean algorithm for polynomials to the problem of computing multiplicative inverses in F ŒX=.f /, where f 2 F ŒX with ` WD deg.f / > 0. Given h 2 F ŒX with deg.h/ < `, using O.`2 / operations in F , we can determine if h is relatively prime to f , and if so, compute h 1 mod f as follows. We run the extended Euclidean algorithm on input f; h, obtaining polynomials d; s; t such that d D gcd.f; h/ and f s C ht D d . If d ¤ 1, then h does not have a multiplicative inverse modulo f . Otherwise, if d D 1, then t is a multiplicative inverse of h modulo f . Moreover, by part (v) of Theorem 17.4, we have deg.t / < `, and so t D h 1 mod f .
17.4 Computing modular inverses and Chinese remaindering
471
We also observe that the Chinese remainder theorem for polynomials (Theorem 16.18) can be made computationally effective as well: Theorem 17.6 (Effective Chinese remainder theorem). Suppose we are given polynomials f1 ; : : : ; fk 2 F ŒX and g1 ; : : : ; gk 2 F ŒX, where the family ffi gkiD1 is pairwise relatively prime, and where deg.fi / > 0 and deg.gi / < deg.fi / for Q i D 1; : : : ; k. Let f WD kiD1 fi . Then using O.len.f /2 / operations in F , we can compute the unique polynomial g 2 F ŒX satisfying deg.g/ < deg.f / and g gi .mod fi / for i D 1; : : : ; k. Proof. Exercise (just use the formulas given after Theorem 16.18). Polynomial interpolation We remind the reader of the discussion following Theorem 16.18, where the point was made that when fi D X xi and gi D yi , for i D 1; : : : ; k, then the Chinese remainder theorem for polynomials reduces to Lagrange interpolation. Thus, Theorem 17.6 says that given distinct elements x1 ; : : : ; xk 2 F , along with elements y1 ; : : : ; yk 2 F , we can compute the unique polynomial g 2 F ŒX of degree less than k such that g.xi / D yi .i D 1; : : : ; k/; using O.k 2 / operations in F . It is perhaps worth noting that we could also solve the polynomial interpolation problem using Gaussian elimination, by inverting the corresponding Vandermonde matrix. However, this algorithm would use O.k 3 / operations in F . This is a specific instance of a more general phenomenon: there are many computational problems involving polynomials over fields that can be solved using Gaussian elimination, but which can be solved more efficiently using more specialized algorithmic techniques. Speeding up algorithms via modular computation In §4.4, we discussed how the Chinese remainder theorem could be used to speed up certain types of computations involving integers. The example we gave was the multiplication of integer matrices. We can use the same idea to speed up certain types of computations involving polynomials. For example, if one wants to multiply two matrices whose entries are elements of F ŒX, one can use the Chinese remainder theorem for polynomials to speed things up. This strategy is most easily implemented if F is sufficiently large, so that we can use polynomial evaluation
472
Polynomial arithmetic and applications
and interpolation directly, and do not have to worry about constructing irreducible polynomials. We leave the details as an exercise. E XERCISE 17.8. You are give two matrices A; B 2 F ŒXmm . All entries of A and B are polynomials of degree at most `. Assume that jF j 2` C 1. Using polynomial evaluation and interpolation, show how to compute the product matrix C D A B using O.m2 `2 C m3 `/ operations in F . Compare this to the cost of computing C directly, which would be O.m3 `2 /. 17.5 Rational function reconstruction and applications Throughout this section, F denotes a field. We next state and prove the polynomial analog of Theorem 4.8. As we are now “reconstituting” a rational function, rather than a rational number, we call this procedure rational function reconstruction. Because of the relative simplicity of polynomials compared to integers, the rational reconstruction theorem for polynomials is a bit “sharper” than the rational reconstruction theorem for integers, and much simpler to prove. To state the result precisely, let us introduce some notation. For polynomials g; h 2 F ŒX with deg.g/ deg.h/ and g ¤ 0, let us define ˚ `C1 EEA.g; h/ WD .ri ; si ; ti / i D0 ; where ri , si , and ti , for i D 0; : : : ; ` C 1, are defined as in Theorem 17.4. Theorem 17.7 (Rational function reconstruction). Let f; h 2 F ŒX be polynomials, and let r ; t be non-negative integers, such that deg.h/ < deg.f / and r C t deg.f /: Further, suppose there exist polynomials r; s; t 2 F ŒX such that r D f s C ht; deg.r/ < r ; and 0 deg.t / t : Let EEA.f; h/ D f.ri ; si ; ti /gi`C1 D0 , and let j be the smallest index (among 0; : : : ; ` C 1) such that deg.rj / < r , and set r 0 WD rj ; s 0 WD sj ; and t 0 WD tj : Then for some non-zero polynomial q 2 F ŒX, we have we have r D r 0 q; s D s 0 q; t D t 0 q:
17.5 Rational function reconstruction and applications
473
Proof. Since deg.r0 / D deg.f / r > 1 D deg.r`C1 /, the value of j is well defined, and moreover, j 1, deg.rj 1 / r , and tj ¤ 0. From the equalities rj D f sj C htj and r D f s C ht , we have the two congruences: rj htj .mod f /; r ht .mod f /: Subtracting t times the first from tj times the second, we obtain rtj rj t .mod f /: This says that f divides rtj rj t. We want to show that, in fact, rtj rj t D 0. To this end, first observe that by part (iv) of Theorem 17.4 and the inequality deg.rj 1 / r , we have deg.tj / D deg.f /
deg.rj
1/
deg.f /
r :
Combining this with the inequality deg.r/ < r , we see that deg.rtj / D deg.r/ C deg.tj / < deg.f /: Furthermore, using the inequalities deg.rj / < r ; deg.t / t ; and r C t deg.f /; we see that deg.rj t / D deg.rj / C deg.t / < deg.f /; and it immediately follows that deg.rtj Since f divides rtj
rj t and deg.rtj rtj
rj t / < deg.f /: rj t / < deg.f /, the only possibility is that rj t D 0:
The rest of the proof follows exactly the same line of reasoning as in the last paragraph in the proof of Theorem 4.8, as the reader may easily verify. 17.5.1 Application: recovering rational functions from their reversed Laurent series We now discuss the polynomial analog of the application in §4.6.1. This is an entirely straightforward translation of the results in §4.6.1, but we shall see in the next chapter that this problem has its own interesting applications. Suppose Alice knows a rational function z D s=t 2 F .X/, where s and t are
474
Polynomial arithmetic and applications
polynomials with deg.s/ < deg.t /, and tells Bob some of the high-order coefficients of the reversed Laurent series (see §16.8) representing z in F ..X 1 //. We shall show that if deg.t / ` and Bob is given the bound ` on deg.t /, along with the high-order 2` coefficients of z, then Bob can determine z, expressed as a rational function in lowest terms. P i So suppose that z D s=t D 1 i D1 zi X , and that Alice tells Bob the coefficients z1 ; : : : ; z2` . Equivalently, Alice gives Bob the polynomial h WD z1 X2`
1
C C z2`
1X
C z2` :
Also, let us define f WD X2` . Here is Bob’s algorithm for recovering z: 1. Run the extended Euclidean algorithm on input f; h to obtain EEA.f; h/, and apply Theorem 17.7 with f , h, r WD `, and t WD `, to obtain the polynomials r 0 ; s 0 ; t 0 . 2. Output s 0 ; t 0 . We claim that z D s 0 =t 0 . To prove this, first observe that h D bf zc D bf s=tc (see Theorem 16.31). So if we set r WD f s mod t, then we have r D fs
ht; deg.r/ < r ; 0 deg.t / t ; and r C t deg.f /:
It follows that the polynomials s 0 ; t 0 from Theorem 17.7 satisfy s D s 0 q and t D t 0 q for some non-zero polynomial q, and thus, s 0 =t 0 D s=t, which proves the claim. We may further observe that since the extended Euclidean algorithm guarantees that gcd.s 0 ; t 0 / D 1, not only do we obtain z, but we obtain z expressed as a fraction in lowest terms. It is clear that this algorithm takes O.`2 / operations in F . 17.5.2 Application: polynomial interpolation with errors We now discuss the polynomial analog of the application in §4.6.2. If we “encode” a polynomial g 2 F ŒX, with deg.g/ < k, as the sequence .y1 ; : : : ; yk / 2 F k , where yi D g.xi /, then we can efficiently recover g from this encoding, using an algorithm for polynomial interpolation. Here, of course, the xi ’s are distinct elements of F (which must have at least k elements, of course). Now suppose that Alice encodes g as .y1 ; : : : ; yk /, and sends this encoding to Bob, but that some, say at most `, of the yi ’s may be corrupted during transmission. Let .z1 ; : : : ; zk / denote the vector actually received by Bob. Here is how we can use Theorem 17.7 to recover the original value of g from .z1 ; : : : ; zk /, assuming: the original polynomial g has degree less than m,
17.5 Rational function reconstruction and applications
475
at most ` errors occur in transmission, and k 2` C m. Let us set fi WD X xi for i D 1; : : : ; k, and f WD f1 fk . Now, suppose Bob obtains the corrupted encoding .z1 ; : : : ; zk /. Here is what Bob does to recover g: 1. Interpolate, obtaining a polynomial h, with deg.h/ < k and h.xi / D zi for i D 1; : : : ; k. 2. Run the extended Euclidean algorithm on input f; h to obtain EEA.f; h/, and apply Theorem 17.7 with f , h, r WD m C ` and t WD `, to obtain the polynomials r 0 ; s 0 ; t 0 . 3. If t 0 j r 0 , output r 0 =t 0 ; otherwise, output “error.” We claim that the above procedure outputs g, under the assumptions listed above. To see this, let t be the product of the fi ’s for those values of i where an error occurred. Now, assuming at most ` errors occurred, we have deg.t / `. Also, let r WD gt, and note that deg.r/ < m C `. We claim that r ht .mod f /:
(17.1)
To show that (17.1) holds, it suffices to show that gt ht .mod fi /
(17.2)
for all i D 1; : : : ; k. To show this, consider first an index i at which no error occurred, so that yi D zi . Then gt yi t .mod fi / and ht zi t yi t .mod fi /, and so (17.2) holds for this i . Next, consider an index i for which an error occurred. Then by construction, gt 0 .mod fi / and ht 0 .mod fi /, and so (17.2) holds for this i . Thus, (17.1) holds, from which it follows that the values r 0 ; t 0 obtained from Theorem 17.7 satisfy r0 r gt D D D g: 0 t t t One easily checks that both the procedures to encode and decode a value g run in time O.k 2 /. The above scheme is an example of an error correcting code called a Reed–Solomon code. 17.5.3 Applications to symbolic algebra Rational function reconstruction has applications in symbolic algebra, analogous to those discussed in §4.6.3. In that section, we discussed the application of solving systems of linear equations over the integers using rational reconstruction. In exactly the same way, one can use rational function reconstruction to solve systems of linear equations over F ŒX — the solution to such a system of equations will be a vector whose entries are elements of F .X/, the field of rational functions.
476
Polynomial arithmetic and applications
E XERCISE 17.9. Consider again the secret sharing problem, as discussed in Example 8.28. There, we presented a scheme that distributes shares of a secret among several parties in such a way that no coalition of k or fewer parties can reconstruct the secret, while every coalition of k C 1 parties can. Now suppose that some parties may be corrupt: in the protocol to reconstruct the secret, a corrupted party may contribute an incorrect share. Show how to modify the protocol in Example 8.28 so that if shares are distributed among several parties, then (a) no coalition of k or fewer parties can reconstruct the secret, and (b) if at most k parties are corrupt, then every coalition of 3k C1 parties (which may include some of the corrupted parties) can correctly reconstruct the secret. The following exercises are the polynomial analogs of Exercises 4.17, 4.19, and 4.20. E XERCISE 17.10. Let F be a field. Show that given polynomials s; t 2 F ŒX and integer k, with deg.s/ < deg.t / and k > 0, we can compute the kth coefficient in the reversed Laurent series representing s=t using O.len.k/ len.t /2 / operations in F. E XERCISE 17.11. Let F be a field. Let z 2 F ..X 1 // be a reversed Laurent series whose coefficient sequence is ultimately periodic. Show that z 2 F .X/. E XERCISE 17.12. Let F be a field. Let z D s=t, where s; t 2 F ŒX, deg.s/ < deg.t /, and gcd.s; t/ D 1. (a) Show that if F is finite, there exist integers k; k 0 such that 0 k < k 0 and 0 sXk sXk .mod t /. (b) Show that for integers k; k 0 with 0 k < k 0 , the sequence of coefficients of the reversed Laurent series representing z is .k; k 0 k/-periodic if and 0 only if sXk sXk .mod t /. (c) Show that if F is finite and X − t, then the reversed Laurent series representing z is purely periodic with period equal to the multiplicative order of ŒX t 2 .F ŒX=.t // . (d) More generally, show that if F is finite and t D Xk t 0 , with X − t 0 , then the reversed Laurent series representing z is ultimately periodic with pre-period k and period equal to the multiplicative order of ŒX t 0 2 .F ŒX=.t 0 // . 17.6 Faster polynomial arithmetic ./ The algorithms discussed in §3.5 for faster integer arithmetic are easily adapted to polynomials over a ring. Throughout this section, R denotes a non-trivial ring.
17.6 Faster polynomial arithmetic ./
477
E XERCISE 17.13. State and re-work the analog of Exercise 3.41 for RŒX. Your algorithm should multiply two polynomials over R of length at most ` using O.`log2 3 / operations in R. It is in fact possible to multiply polynomials over R of length at most ` using O.` len.`/ len.len.`/// operations in R — we shall develop some of the ideas that lead to such a result below in Exercises 17.22–17.25 (see also the discussion in §17.7). In Exercises 17.14–17.20 below, assume that we have an algorithm that multiplies two polynomials over R of length at most ` using at most M.`/ operations in R, where M is a well-behaved complexity function (as defined in §3.5). E XERCISE 17.14. State and re-work the analog of Exercises 3.46 and 3.47 for RŒX. E XERCISE 17.15. This problem is the analog of Exercise 3.48 for RŒX. Let us first define the notion of a “floating point” reversed Laurent series z, O which is represented as a pair .g; e/, where g 2 RŒX and e 2 Z — the value of zO is gXe 2 R..X 1 //, and we call len.g/ the precision of z. O We say that zO is a length k approximation of z 2 R..X 1 // if zO has precision k and zO D .1 C /z for 2 R..X 1 // with deg./ k, which is the same as saying that the high-order k coefficients of zO and z are equal. Show that given h 2 RŒX with lc.h/ 2 R , and positive integer k, we can compute a length k approximation of 1= h 2 R..X 1 // using O.M.k// operations in R. Hint: using Newton iteration, show how to go from a length t approximation of 1= h to a length 2t approximation, making use of just the high-order 2t coefficients of h, and using O.M.t // operations in R. E XERCISE 17.16. State and re-work the analog of Exercise 3.49 for RŒX. E XERCISE 17.17. State and re-work the analog of Exercise 3.50 for RŒX. Conclude that a polynomial of length at most k can be evaluated at k points using O.M.k/ len.k// operations in R. E XERCISE 17.18. State and re-work the analog of Exercise 3.51 for RŒX, assuming that R is a field of odd characteristic. E XERCISE 17.19. State and re-work the analog of Exercise 3.52 for RŒX. Assume that 2R 2 R . The next two exercises develop a useful technique known as Kronecker substitution. P 1 Pm 1 i i E XERCISE 17.20. Let g; h 2 RŒX; Y with g D m i D0 gi Y and h D iD0 hi Y ; where each gi and hi is a polynomial in X of degree less than k. The product f WD
478
Polynomial arithmetic and applications
P 2 i gh 2 RŒX; Y may be written f D 2m iD0 fi Y ; where each fi is a polynomial in X. Show how to compute f , given g and h, using O.M.km// operations in R. Hint: for an appropriately chosen integer t > 0, first convert g; h to g; Q hQ 2 RŒX, Pm 1 P m 1 t i t i where gQ WD i D0 gi X and hQ WD i D0 hi X I next, compute fQ WD gQ hQ 2 RŒX; finally, “read off” the fi ’s from the coefficients of fQ. E XERCISE 17.21. Assume that integers of length at most ` can be multiplied in x .`/, where M x is a well-behaved complexity function. Let g; h 2 ZŒX time M Pm 1 Pm 1 i i with g D i D0 ai X and h D i D0 bi X ; where each ai and bi is a nonnegative integer, strictly less than 2k . The product f WD gh 2 ZŒX may be P 2 i written f D 2m i D0 ci X ; where each ci is a non-negative integer. Show how to x ..k C len.m//m// operations in R. Hint: compute f , given g and h, using O.M for an appropriately chosen integer t > 0, first convert g; h to a; b 2 Z, where P P 1 ti a WD imD01 ai 2t i and b WD m i D0 bi 2 I next, compute c WD ab 2 Z; finally, “read off” the ci ’s from the bits of c. The following exercises develop an important algorithm for multiplying polynomials in almost-linear time. For an integer n 0, let us call ! 2 R a primitive n 1 2n th root of unity if n 1 and ! 2 D 1R , or n D 0 and ! D 1R ; if 2R ¤ 0R , then in particular, ! has multiplicative order 2n . For n 0, and ! 2 R n n a primitive 2n th root of unity, let us define the R-linear map En;! W R2 ! R2 n that sends the vector .a0 ; : : : ; a2n 1 / to the vector .g.1R /; g.!/; : : : ; g.! 2 1 //, P n where g WD 2i D0 1 ai Xi 2 RŒX. E XERCISE 17.22. Suppose 2R 2 R and ! 2 R is a primitive 2n th root of unity. (a) Let k be any integer, and consider gcd.k; 2n /, which must be of the form 2m for some m D 0; : : : ; n. Show that ! k is a primitive 2n m th root of unity. (b) Show that if n 1, then ! (c) Show that ! k
1R 2 R .
1R 2 R for all integers k 6 0 .mod 2n /.
(d) Show that for every integer k, we have n 1 n 2X 2R if k 0 .mod 2n /, ki ! D 0R if k 6 0 .mod 2n /. i D0
n
(e) Let M2 be the 2-multiplication map on R2 , which is a bijective, R-linear map. Show that En;! B En;!
1
D M2n D En;!
1
B En;! ;
and conclude that En;! is bijective, with M2 n B En;! 1 being its inverse. Hint: write down the matrices representing the maps En;! and En;! 1 .
17.6 Faster polynomial arithmetic ./
479
E XERCISE 17.23. This exercise develops a fast algorithm, called the fast Fourier transform or FFT, for computing the function En;! . This is a recursive algorithm FFT.n; !I a0 ; : : : ; a2n 1 / that takes as input an integer n 0, a primitive 2n th root of unity ! 2 R, and elements a0 ; : : : ; a2n 1 2 R, and runs as follows: if n D 0 then return a0 else .˛0 ; : : : ; ˛2n 1 1 / FFT.n 1; ! 2 I a0 ; a2 ; : : : ; a2n .ˇ0 ; : : : ; ˇ2n 1 1 / FFT.n 1; ! 2 I a1 ; a3 ; : : : ; a2n for i 0 to 2n 1 1 do
i ˛i C ˇi ! i , i C2n 1 ˛i ˇi ! i return . 0 ; : : : ; 2n 1 / Show that this algorithm correctly computes En;! .a0 ; : : : ; a2n operations in R.
1/
2/ 1/
using O.2n n/
E XERCISE 17.24. Assume 2R 2 R . Suppose that we are given two polynomials g; h 2 RŒX of length at most `, along with a primitive 2n th root of unity ! 2 R, P2n 1 i where 2` 2n < 4`. Let us “pad” g and h, writing g D i D0 ai X and P2n 1 h D i D0 bi Xi , where ai and bi are zero for i `. Show that the following algorithm correctly computes the product of g and h using O.` len.`// operations in R: .˛0 ; : : : ; ˛2n 1 / .ˇ0 ; : : : ; ˇ2n 1 / . 0 ; : : : ; 2n 1 / .c0 ; : : : ; c2n 1 / P 2 i output i2` D0 ci X
FFT.n; !I a0 ; : : : ; a2n 1 / FFT.n; !I b0 ; : : : ; b2n 1 / .˛0 ˇ0 ; : : : ; ˛2n 1 ˇ2n 1 / 2Rn FFT.n; ! 1 I 0 ; : : : ; 2n
1/
Also, argue more carefully that the algorithm performs O.` len.`// additions/subtractions in R, O.` len.`// multiplications in R by powers of !, and O.`/ other multiplications in R. E XERCISE 17.25. Assume 2R 2 R . In this exercise, we use the FFT to develop an algorithm that multiplies polynomials over R of length at most ` using O.` len.`/ˇ / operations in R, where ˇ is a constant. Unlike the previous exercise, we do not assume that R contains any particular primitive roots of unity; rather, the algorithm will create them are of length p “out of thin air.” Suppose that g; h 2 RŒX Pm 1 at most `. Set k WD b `=2c, m WD d`=ke. We may write g D i D0 gi Xki and P 1 ki h D m i D0 hi X , where the gi ’s and hi ’s are polynomials of length at most k. n 1 Let n be the integer determined by 2m 2n < 4m. Let q WD X2 C 1R 2 RŒX, E WD RŒX=.q/, and ! WD ŒXq 2 E.
480
Polynomial arithmetic and applications
(a) Show that ! is a primitive 2n th root of unity in E, and that given an element ı 2 E and an integer i between 0 and 2n 1, we can compute ı! i 2 E using O.`1=2 / operations in R. P P 1 i (b) Let gN WD imD01 Œgi q Yi 2 EŒY and hN WD m i D0 Œhi q Y 2 EŒY. Using the FFT (over E), show how to compute fN WD gN hN 2 EŒY by computing O.`1=2 / products in RŒX of polynomials of length O.`1=2 /, along with O.` len.`// additional operations in R. (c) Show how to compute the coefficients of f WD gh 2 RŒX from the value fN 2 EŒY computed in part (b), using O.`/ operations in R. (d) Based on parts (a)–(c), we obtain a recursive multiplication algorithm: on inputs of length at most `, it performs at most ˛0 ` len.`/ operations in R, and calls itself recursively on at most ˛1 `1=2 subproblems, each of length at most ˛2 `1=2 ; here, ˛0 , ˛1 and ˛2 are constants. If we just perform one level of recursion, and immediately switch to a quadratic multiplication algorithm, we obtain an algorithm whose operation count is O.`1:5 /. If we perform two levels of recursion, this is reduced to O.`1:25 /. For practical purposes, this is probably enough; however, to get an asymptotically better complexity bound, we can let the algorithm recurse all the way down to inputs of some (appropriately chosen) constant length. Show that if we do this, the operation count of the recursive algorithm is O.` len.`/ˇ / for some constant ˇ (whose value depends on ˛1 and ˛2 ). The approach used in the previous exercise was a bit sloppy. With a bit more care, one can use the same ideas to get an algorithm that multiplies polynomials over R of length at most ` using O.` len.`/ len.len.`/// operations in R, assuming 2R 2 R . The next exercise applies similar ideas, but with a few twists, to the problem of integer multiplication. E XERCISE 17.26. This exercise uses the FFT to develop a linear-time algorithm for integer multiplication; however, a rigorous analysis depends on an unproven conjecture (which follows from a generalization of the Riemann hypothesis). Suppose we want to multiply two positive integers a and b, each of length at most ` (represented internally using the data structure described in §3.3). Throughout this exercise, assume that all computations are done on a RAM, and that arithmetic on integers of length O.len.`// takes time O.1/. Let k be an integer parameter P 1 ki and with k D ‚.len.`//, and let m WD d`=ke. We may write a D m i D0 ai 2 Pm 1 b D i D0 bi 2ki , where 0 ai < 2k and 0 bi < 2k . Let n be the integer determined by 2m 2n < 4m. (a) Assuming Conjecture 5.22, and assuming a deterministic, polynomial-time primality test (such as the one to be presented in Chapter 21), show how to
17.6 Faster polynomial arithmetic ./
481
efficiently generate a prime p 1 .mod 2n / and an element ! 2 Zp of multiplicative order 2n , such that 22k m < p `O.1/ : Your algorithm should be probabilistic, and run in expected time polynomial in len.`/. (b) Assuming you have computed p and ! as in part (a), let g WD Pm 1 Pm 1 i i i D0 Œai p X 2 Zp ŒX and h WD i D0 Œbi p X 2 Zp ŒX, and show how to compute f WD gh 2 Zp ŒX in time O.`/ using the FFT (over Zp ). Here, you may store elements of Zp in single memory cells, so that operations in Zp take time O.1/. (c) Assuming you have computed f 2 Zp ŒX as in part (b), show how to obtain c WD ab in time O.`/. (d) Conclude that assuming Conjecture 5.22, we can multiply two integers of length at most ` on a RAM in time O.`/. Note that even if one objects to our accounting practices, and insists on charging O.len.`/2 / time units for arithmetic on numbers of length O.len.`//, the algorithm in the previous exercise runs in time O.` len.`/2 /, which is “almost” linear time. E XERCISE 17.27. Continuing with the previous exercise: (a) Show how the algorithm presented there can be implemented on a RAM that has only built-in addition, subtraction, and branching instructions, but no multiplication or division instructions, and still run in time O.`/. Also, memory cells should store numbers of length at most len.`/ C O.1/. Hint: represent elements of Zp as sequences of base-2t digits, where t ˛ len.`/ for some constant ˛ < 1; use table lookup to multiply t-bit numbers, and to perform 2t-by-t-bit divisions — for ˛ sufficiently small, you can build these tables in time o.`/. (b) Using Theorem 5.23, show how to make this algorithm fully deterministic and rigorous, provided that on inputs of length `, it is provided with a certain bit string ` of length O.len.`// (this is called a non-uniform algorithm). E XERCISE 17.28. This exercise shows how the algorithm in Exercise 17.26 can be made quite concrete, and fairly practical, as well. (a) The number p WD 259 27 C 1 is a 64-bit prime. Show how to use this value of p in conjunction with the algorithm in Exercise 17.26 with k D 20 and any value of ` up to 227 . (b) The numbers p1 WD 230 3 C 1, p2 WD 228 13 C 1, and p3 WD 227 29 C 1 are
482
Polynomial arithmetic and applications
32-bit primes. Show how to use the Chinese remainder theorem to modify the algorithm in Exercise 17.26, so that it uses the three primes p1 ; p2 ; p3 , and so that it works with k D 32 and any value of ` up to 231 . This variant may be quite practical on a 32-bit machine with built-in instructions for 32-bit multiplication and 64-by-32-bit division. The previous three exercises indicate that we can multiply integers in essentially linear time, both in theory and in practice. As mentioned in §3.6, there is a different, fully deterministic and rigorously analyzed algorithm that multiplies integers in linear time on a RAM. In fact, that algorithm works on a very restricted type of machine called a “pointer machine,” which can be simulated in “real time” on a RAM with a very restricted instruction set (including the type in the previous exercise). That algorithm works with finite approximations to complex roots of unity, rather than roots of unity in a finite field. We close this section with a cute application of fast polynomial multiplication to the problem of factoring integers. E XERCISE 17.29. Let n be a large, positive integer. We can factor n using trial division in time n1=2Co.1/ ; however, using fast polynomial arithmetic in Zn ŒX, one can get a simple, deterministic, and rigorous algorithm that factors n in time n1=4Co.1/ . Note that all of the factoring algorithms discussed in Chapter 15, while faster, are either probabilistic, or deterministic but heuristic. Assume that we can multiply polynomials in Zn ŒX of length at most ` using M.`/ operations in Zn , where M is a well-behaved complexity function, and M.`/ D `1Co.1/ (the algorithm from Exercise 17.25 would suffice). (a) Let ` be a positive integer, and for i D 1; : : : ; `, let ai WD
`Y 1
.i `
j / mod n:
j D0
Using fast polynomial arithmetic, show how to compute all of the integers a1 ; : : : ; a` in time `1Co.1/ len.n/O.1/ . (b) Using the result of part (a), show how to factor n in time n1=4Co.1/ using a deterministic algorithm. 17.7 Notes Exercise 17.3 is based on an algorithm of Brent and Kung [20]. Using fast matrix arithmetic, Brent and Kung show how this problem can be solved using O.`.!C1/=2 / operations in R, where ! is the exponent for matrix multiplication (see §14.6), and so .! C 1/=2 < 1:7.
17.7 Notes
483
Reed–Solomon codes were first proposed by Reed and Solomon [79], although the decoder presented here was developed later. Theorem 17.7 was proved by Mills [66]. The Reed–Solomon code is just one way of detecting and correcting errors — we have barely scratched the surface of this subject. Just as in the case of integer arithmetic, the basic “pencil and paper” quadratictime algorithms discussed in this chapter for polynomial arithmetic are not the best possible. The fastest known algorithms for multiplication of polynomials of length at most ` over a ring R take O.` len.`/ len.len.`/// operations in R. These algorithms are all variations on the basic FFT algorithm (see Exercise 17.24), but work without assuming that 2R 2 R or that R contains any particular primitive roots of unity (we developed some of the ideas in Exercise 17.25). The Euclidean and extended Euclidean algorithms for polynomials over a field F can be implemented so as to take O.` len.`/2 len.len.`/// operations in F , as can the algorithms for Chinese remaindering and rational function reconstruction. See the book by von zur Gathen and Gerhard [38] for details (as well for an analysis of the Euclidean algorithm for polynomials over the field of rational numbers and over function fields). Depending on the setting and many implementation details, such asymptotically fast algorithms for multiplication and division can be significantly faster than the quadratic-time algorithms, even for quite moderately sized inputs of practical interest. However, the fast Euclidean algorithms are only useful for significantly larger inputs.
18 Linearly generated sequences and applications
In this chapter, we develop some of the theory of linearly generated sequences. As an application, we develop an efficient algorithm for solving sparse systems of linear equations, such as those that arise in the subexponential-time algorithms for discrete logarithms and factoring in Chapter 15. These topics illustrate the beautiful interplay between the arithmetic of polynomials, linear algebra, and the use of randomization in the design of algorithms. 18.1 Basic definitions and properties Let F be a field, let V be an F -vector space, and consider an infinite sequence D f˛i g1 i D0 where ˛i 2 V for i D 0; 1; 2 : : : : We say that is linearly generated (over F ) if there exist scalars c0 ; : : : ; ck 1 2 F such that the following recurrence relation holds: k X1 ˛kCi D cj ˛j Ci (for i D 0; 1; 2; : : :): j D0
In this case, all of the elements of the sequence are determined by the initial segment ˛0 ; : : : ; ˛k 1 , together with the coefficients c0 ; : : : ; ck 1 defining the recurrence relation. The general problem we consider is this: how to determine the coefficients defining such a recurrence relation, given a sufficiently long initial segment of . To study this problem, it turns out to be very useful to rephrase the problem slightly. P Let g 2 F ŒX be a polynomial of degree, say, k, and write g D jkD0 aj Xj . Next,
484
485
18.1 Basic definitions and properties
define g?
WD
k X
aj ˛j :
j D0
Then it is clear that is linearly generated if and only if there exists a non-zero polynomial g such that .Xi g/ ?
D 0 (for i D 0; 1; 2; : : :):
(18.1)
Indeed, if there is such a non-zero polynomial g, then we can take c0 WD
.a0 =ak /; c1 WD
.a1 =ak /; : : : ; ck
1
WD
.ak
1 =ak /
as coefficients defining the recurrence relation for . We call a polynomial g satisfying (18.1) a generating polynomial for . The sequence will in general have many generating polynomials. Note that the zero polynomial is technically considered a generating polynomial, but is not a very interesting one. Let G. / be the set of all generating polynomials for . Theorem 18.1. G. / is an ideal of F ŒX. Proof. First, note that for all polynomials g; h 2 F ŒX, we have .g C h/ ? D .g? /C.h? /—this is clear from the definitions. It is also clear that for all c 2 F and g 2 F ŒX, we have .cg/ ? D c .g ? /. From these two observations, it is immediately clear that G. / is closed under addition and scalar multiplication. It is also clear from the definition that G. / is closed under multiplication by X; indeed, if .Xi g/ ? D 0 for all i 0, then certainly, .Xi .Xg// ? D .Xi C1 g/ ? D 0 for all i 0. But any non-empty subset of F ŒX that is closed under addition, multiplication by elements of F , and multiplication by X is an ideal of F ŒX (see Exercise 7.26). Since all ideals of F ŒX are principal, it follows that G. / is the ideal of F ŒX generated by some polynomial 2 F ŒX — we can make this polynomial unique by choosing the monic associate (if it is non-zero), and we call this polynomial the minimal polynomial of . Thus, a polynomial g 2 F ŒX is a generating polynomial for if and only if divides g; in particular, is linearly generated if and only if ¤ 0. We can now restate our main objective as follows: given a sufficiently long initial segment of a linearly generated sequence, determine its minimal polynomial. Example 18.1. Of course, one can always define a linearly generated sequence by simply choosing an initial segment ˛0 ; ˛1 ; : : : ; ˛k 1 , along with scalars c0 ; : : : ; ck 1 2 F defining the recurrence relation. One can enumerate as many
486
Linearly generated sequences and applications
elements of the sequence as one wants by using storage for k elements of V , along with storage for the scalars c0 ; : : : ; ck 1 , as follows: .ˇ0 ; : : : ; ˇk 1 / .˛0 ; : : : ; ˛k 1 / repeat output ˇ0 Pk 1 ˇ0 j D0 cj ˇj .ˇ0 ; : : : ; ˇk 1 / .ˇ1 ; : : : ; ˇk forever
1; ˇ
0/
Because of the structure of the above algorithm, linearly generated sequences are sometimes also called shift register sequences. Also observe that if F is a finite field, and V is finite dimensional, the value stored in the “register” .ˇ0 ; : : : ; ˇk 1 / must repeat at some point, from which it follows that the linearly generated sequence must be ultimately periodic (see definitions above Exercise 4.18). Example 18.2. Linearly generated sequences can also arise in a natural way, as this example and the next illustrate. Let E WD F ŒX=.f /, where f 2 F ŒX is a monic polynomial of degree ` > 0, and let ˛ be an element of E. Consider the sequence Pk j WD f˛ i g1 j D0 aj X 2 F ŒX, we i D0 of powers of ˛. For every polynomial g D have k X g? D aj ˛ j D g.˛/: j D0
Now, if g.˛/ D 0, then clearly .Xi g/ ? D ˛ i g.˛/ D 0 for all i 0. Conversely, if .Xi g/ ? D 0 for all i 0, then in particular, g.˛/ D 0. Thus, g is a generating polynomial for if and only if g.˛/ D 0. It follows that the minimal polynomial of is the same as the minimal polynomial of ˛ over F , as defined in §16.5. Furthermore, ¤ 0, and the degree m of may be characterized as the smallest positive integer m such that f˛ i gm i D0 is linearly dependent; moreover, as E has dimension ` over F , we must have m `. Example 18.3. Let V be a vector space over F of dimension ` > 0, and let W V ! V be an F -linear map. Let ˇ 2 V , and consider the sequence WD f˛i g1 i D0 , i where ˛i D .ˇ/; that is, ˛0 D ˇ, ˛1 D .ˇ/, ˛2 D . .ˇ//, and so on. For P every polynomial g D jkD0 aj Xj 2 F ŒX, we have g?
D
k X j D0
aj j .ˇ/;
487
18.1 Basic definitions and properties
and for every i 0, we have i
.X g/ ?
D
k X j D0
aj
i Cj
.ˇ/ D
i
X k
aj .ˇ/ D i .g ? j
/:
j D0
Thus, if g ? D 0, then clearly .Xi g/ ? D i .g ? / D i .0/ D 0 for all i 0. Conversely, if .Xi g/ ? D 0 for all i 0, then in particular, g ? D 0. Thus, g is a generating polynomial for if and only if g ? D 0. The minimal polynomial of is non-zero and its degree m is at most `; indeed, m may be characterized as the least non-negative integer such that f i .ˇ/gm i D0 is linearly dependent, and since V has dimension ` over F , we must have m `. The previous example can be seen as a special case of this one, by taking V to be E, to be the ˛-multiplication map on E, and setting ˇ to 1. The problem of computing the minimal polynomial of a linearly generated sequence can always be solved by means of Gaussian elimination. For example, the minimal polynomial of the sequence discussed in Example 18.2 can be computed using the algorithm described in §17.2. The minimal polynomial of the sequence discussed in Example 18.3 can be computed in a similar manner. Also, Exercise 18.3 below shows how one can reformulate another special case of the problem so that it is easily solved by Gaussian elimination. However, in the following sections, we will present algorithms for computing minimal polynomials for certain types of linearly generated sequences that are much more efficient than any algorithm based on Gaussian elimination. E XERCISE 18.1. Show that the only sequence for which 1 is a generating polynomial is the “all zero” sequence. E XERCISE 18.2. Let D f˛i g1 i D0 be a sequence of elements of an F -vector space V . Further, suppose that has non-zero minimal polynomial . (a) Show that for all polynomials g; h 2 F ŒX, if g h .mod /, then g ? h? . (b) Let m WD deg. /. Show that if g 2 F ŒX and .Xi g/ ? 0; : : : ; m 1, then g is a generating polynomial for .
D
D 0 for i D
E XERCISE 18.3. This exercise develops an alternative characterization of linearly generated sequences. Let D fzi g1 sequence of elements of F . Further, i D0 be a P suppose that has minimal polynomial D jmD0 cj Xj with m > 0 and cm D 1.
488
Linearly generated sequences and applications
˙
Define the matrix
A WD
z0 z1 :: :
zm
z1 z2 :: : 1
:: :
zm 1 zm :: :
zm
z2m
2 F mm
2
and the vector w WD .zm ; : : : ; z2m
1/
2 F 1m :
Show that v D . c0 ; : : : ; cm
1/
2 F 1m
is the unique solution to the equation vA D w: Hint: show that the rows of A form a linearly independent family of vectors by making use of Exercise 18.2 and the fact that no polynomial of degree less than m is a generating polynomial for . E XERCISE 18.4. Suppose that you are given c0 ; : : : ; ck z0 ; : : : ; zk 1 2 F . Suppose that for all i 0, we define zkCi WD
k X1
1
2
F and
cj zj Ci :
j D0
Given n 0, show how to compute zn using O.len.n/k 2 / operations in F . E XERCISE 18.5. Let V be a vector space over F , and consider the set V 1 of all infinite sequences f˛i g1 i D0 , where the ˛i ’s are in V . Let us define the scalar product of g 2 F ŒX and 2 V 1 as g
D f.Xi g/ ?
1 g1 : i D0 2 V
Show that with this scalar product, V 1 is an F ŒX-module, and that a polynomial g 2 F ŒX is a generating polynomial for 2 V 1 if and only if g D 0. 18.2 Computing minimal polynomials: a special case We now tackle the problem of computing the minimal polynomial of a linearly generated sequence from a sufficiently long initial segment. We shall first address a special case of this problem, namely, the case where the vector space V is just the field F . In this case, we have D fzi g1 i D0 ;
489
18.2 Computing minimal polynomials: a special case
where zi 2 F for i D 0; 1; 2; : : : : Suppose that we do not know the minimal polynomial of , but we know an upper bound M 0 on its degree. Then it turns out that the initial segment z0 ; z1 ; : : : z2M 1 completely determines , and moreover, we can very efficiently compute given this initial segment. The following theorem provides the essential ingredient. Theorem 18.2. Let D fzi g1 i D0 be a sequence of elements of F , and define the reversed Laurent series z WD
1 X
zi X
.i C1/
2 F ..X
1
//;
i D0
whose coefficients are the elements of the sequence . Then for every g 2 F ŒX, we have g 2 G. / if and only if gz 2 F ŒX. In particular, is linearly generated if and only if z is a rational function, in which case, its minimal polynomial is the denominator of z when expressed as a fraction in lowest terms. Proof. Observe that for every polynomial g 2 F ŒX and every integer i 0, the coefficient of X .i C1/ in the product gz is equal to Xi g ? — just look at the formulas defining these expressions! It follows that g is a generating polynomial for if and only if the coefficients of the negative powers of X in gz are all zero, which is the same as saying that gz 2 F ŒX. Further, if g ¤ 0 and h WD gz 2 F ŒX, then deg.h/ < deg.g/— this follows simply from the fact that deg.z/ < 0 (together with the fact that deg.h/ D deg.g/ C deg.z/). All the statements in the theorem follow immediately from these observations. By virtue of Theorem 18.2, we can compute the minimal polynomial of using the algorithm in §17.5.1 for computing the numerator and denominator of a rational function from its reversed Laurent series expansion. More precisely, we can compute given the bound M on its degree, along with the first 2M elements z0 ; : : : ; z2M 1 of , using O.M 2 / operations in F . Just for completeness, we write down this algorithm: 1. Run the extended Euclidean algorithm on inputs f WD X2M and h WD z0 X2M
1
C z1 X2M
2
C C z2M
1;
and apply Theorem 17.7 with f , h, r WD M , and t WD M , to obtain the polynomials r 0 ; s 0 ; t 0 . 2. Output WD t 0 = lc.t 0 /. E XERCISE 18.6. Suppose F is a finite field and that
WD fzi g1 i D0 is linearly
490
Linearly generated sequences and applications
generated, with minimal polynomial . Further, suppose X − . Show that is purely periodic with period equal to the multiplicative order of ŒX 2 .F ŒX=. // . Hint: use Exercise 17.12 and Theorem 18.2. 18.3 Computing minimal polynomials: a more general case Having dealt with the problem of finding the minimal polynomial of a sequence of elements of F , we address the more general problem, where the elements of lie in a vector space V over F . We shall only deal with a special case of this problem, but it is one which has useful applications: First, we shall assume that V has finite dimension ` > 0 over F . Second, we shall assume that the sequence D f˛i g1 i D0 has full rank, by which we mean the following: if the minimal polynomial of over F has degree m, then f˛i gimD01 is linearly independent. The sequences considered in Examples 18.2 and 18.3 are of this type. Third, we shall assume that F is a finite field. The dual space. To develop the theory behind the approach we are going to present, we need to discuss the dual space DF .V / of V (over F ), which consists of all F -linear maps from V into F . Thus, DF .V / D HomF .V; F /, and is a vector space over F , with addition and scalar multiplication defined point-wise (see Theorem 13.12). We shall call elements of DF .V / projections. Now, fix a basis S D f i g`iD1 for V . As was discussed in §14.2, every element ı 2 V has a unique coordinate vector VecS .ı/ D .c1 ; : : : ; c` / 2 F 1` , where ı D P 1` is a vector space isomorphism. i ci i . Moreover, the map VecS W V ! F To each projection 2 DF .V / we may also associate the coordinate vector ..ı1 /; : : : ; .ı` //> 2 F `1 . If T is the basis for F consisting of the single element 1F , then the coordinate vector of is MatS;T ./, that is, the matrix of relative to the bases S and T . By Theorem 14.4, the map MatS;T W DF .V / ! F `1 is a vector space isomorphism. In working with algorithms that compute with elements of V and DF .V /, we shall assume that such elements are represented using coordinate vectors relative to some convenient, fixed basis. If ı 2 V has coordinate vector .c1 ; : : : ; c` / 2 F 1` , and 2 DF .V / has coordinate vector .d1 ; : : : ; d` /> 2 F `1 , then .ı/ is easily P computed, using O.`/ operations in F , as `iD1 ci di . We now return to the problem of computing the minimal polynomial of the linearly generated sequence D f˛i g1 i D0 . Assume we have a bound M > 0 on the degree of . As we are assuming has full rank, we may assume that M `. For every 2 DF .V /, we may consider the projected sequence WD f.˛i /g1 i D0 .
18.3 Computing minimal polynomials: a more general case
491
Observe that is a generating polynomial for ; indeed, for every polynomial g 2 F ŒX, we have g ? D .g ? /, and hence, for all i 0, we have .Xi / ? D ..Xi / ? / D .0/ D 0. Let 2 F ŒX denote the minimal polynomial of . Since divides every generating polynomial of , and since is a generating polynomial for , it follows that divides . This suggests the following algorithm for efficiently computing the minimal polynomial of , using the first 2M terms of : Algorithm MP. Given the first 2M terms of the sequence following:
D f˛i g1 i D0 , do the
g 1 2 F ŒX repeat choose 2 DF .V / at random compute the first 2M terms of the projected sequence use the algorithm in §18.2 to compute the minimal polynomial of g lcm.g; / until g ? D 0 output g A few remarks on the above procedure are in order: in every iteration of the main loop, g is the least common multiple of a number of divisors of , and hence is itself a divisor of ; in particular, deg.g/ M ; under our assumption that has full rank, and since g is a monic divisor of , if g ? D 0, we may safely conclude that g D ; under our assumption that F is finite, choosing a random element of DF .V / amounts to simply choosing at random the entries of the coordinate vector of , relative to some basis for V ; we also assume that elements of V are represented as coordinate vectors, so that applying a projection 2 DF .V / to an element of V takes O.`/ operations in F ; in particular, in each loop iteration, we can compute the first 2M terms of the projected sequence using O.M `/ operations in F; similarly, adding two elements of V , or multiplying an element of V times a scalar, takes O.`/ operations in F ; in particular, in each loop iteration, we can compute g ? using O.M `/ operations in F . Based on the above observations, it follows that when the algorithm halts, its output is correct, and that the cost of each loop iteration is O.M `/ operations in
492
Linearly generated sequences and applications
F . The remaining question to be answered is this: what is the expected number of iterations of the main loop? The answer to this question is O.1/, which leads to a total expected cost of Algorithm MP of O.M `/ operations in F . The key to establishing that the expected number of iterations of the main loop is constant is provided by the following theorem. Theorem 18.3. Let D f˛i g1 i D0 be a linearly generated sequence over the field F , where the ˛i ’s are elements of a vector space V of finite dimension ` > 0. Let be the minimal polynomial of over F , let m WD deg. /, and assume that has full rank (i.e., f˛i gimD01 is linearly independent). Finally, let F ŒX<m denote the vector space over F consisting of all polynomials in F ŒX of degree less than m. Under the above assumptions, there exists a surjective F -linear map W DF .V / ! F ŒX<m such that for all 2 DF .V /, the minimal polynomial of the projected sequence WD f.˛i /g1 i D0 satisfies D
: gcd. ./; /
Proof. While the statement of this theorem looks a bit complicated, its proof is quite straightforward, given our characterization of linearly generated sequences in Theorem 18.2 in terms of rational functions. We build the linear map as the composition of two linear maps, 0 and 1 . Let us define the map 0 W DF .V / ! F ..X 7!
1 X
1
//
.˛i /X
.iC1/
:
i D0
We also define the map 1 to be the -multiplication map on F ..X 1 //, that is, the map that sends z 2 F ..X 1 // to z 2 F ..X 1 //. The map is just the composition D 1 B 0 . It is clear that both 0 and 1 are F -linear maps, and hence, so is . First, observe that for 2 DF .V /, the series z WD 0 ./ is the series associated with the projected sequence , as in Theorem 18.2. Let be the minimal polynomial of . Since is a generating polynomial for , it is also a generating polynomial for . Therefore, Theorem 18.2 tells us that h WD ./ D z 2 F ŒX<m ; and that is the denominator of z when expressed as a fraction in lowest terms. Now, we have z D h= , and it follows that D = gcd.h; / is this denominator. Second, the hypothesis that f˛i gimD01 is linearly independent implies that dimF .Im 0 / m (see Exercise 14.3). Also, observe that 1 is an injective map (indeed, it is surjective as well). Therefore, dimF .Im / m. In the previous
18.3 Computing minimal polynomials: a more general case
493
paragraph, we observed that Im F ŒX<m , and since dimF .F ŒX<m / D m, we may conclude that Im D F ŒX<m . That proves the theorem. Given the above theorem, we can analyze the expected number of iterations of the main loop of Algorithm MP. First of all, we may as well assume that the degree m of is greater than 0, as otherwise, we are sure to get in the very first iteration. Let 1 ; : : : ; s be the random projections chosen in the first s iterations of Algorithm MP. By Theorem 18.3, each .i / is uniformly distributed over F ŒX<m , and we have g D at the end of loop iteration s if and only if gcd. ; .1 /; : : : ; .s // D 1. Let us define ƒF .s/ to be the probability that gcd. ; f1 ; : : : ; fs / D 1, where f1 ; : : : ; fs are randomly chosen from F ŒX<m . Thus, the probability that we have g D at the end of loop iteration s is equal to ƒF .s/. While one can analyze the quantity ƒF .s/, it turns out to be easier, and sufficient for our purposes, to analyze a different quantity. Let us define ƒm F .s/ to be the probability that gcd.f1 ; : : : ; fs / D 1, where f1 ; : : : ; fs are randomly chosen from F ŒX<m . Clearly, ƒF .s/ ƒm F .s/. Theorem 18.4. If F is a finite field of cardinality q, and m and s are positive integers, then we have ƒm F .s/ D 1
1=q s
1
C .q
1/=q sm :
Proof. For any positive integer n, let Un be the set of all tuples of polynomials .f1 ; : : : ; fs / 2 F ŒXs
which holds for all n 1. Replacing n by n q
s.n 1/
D1C
n X2 kD0
1 in (18.2), we obtain q k un
1 k;
(18.3)
494
Linearly generated sequences and applications
which holds for all n 2, and indeed, holds for n D 1 as well. Subtracting q times (18.3) from (18.2), we deduce that for n 1, q sn
q sn
sC1
D 1 C un
q;
and rearranging terms: un D q sn
q sn
sC1
Cq
1:
Therefore, sm ƒm D1 F .s/ D um =q
1=q s
1
C .q
1/=q sm :
From the above theorem, it follows that for s 1, the probability Ps that Algorithm MP runs for more than s loop iterations is at most 1=q s 1 . If L is the total number of loop iterations, then X X X q 1=q s 1 D 1 C Ps 1 C EŒL D PŒL i D 1 C 3: q 1 s1
s1
i1
Let us summarize all of the above analysis with the following: Theorem 18.5. Let be a sequence of elements of an F -vector space V of finite dimension ` > 0 over F , where F is a finite field. Assume that is linearly generated over F with minimal polynomial 2 F ŒX of degree m, and that has full rank (i.e., the first m terms of form a linearly independent family of elements). Then given an upper bound M on m, along with the first 2M elements of , Algorithm MP correctly computes using an expected number of O.M `/ operations in F . We close this section with the following observation. Suppose the sequence is of the form f i .ˇ/g1 i D0 , where ˇ 2 V and W V ! V is an F -linear map. Suppose that with respect to some basis S for V , elements of V are represented by their coordinate vectors (which are elements of F 1` ), and elements of DF .V / are represented by their coordinate vectors (which are elements of F `1 ). The linear map also has a corresponding matrix A D MatS;S .V; V / 2 F `` , so that evaluating at a point ˛ in V corresponds to multiplying the coordinate vector of ˛ on the right by A. Now, suppose ˇ 2 V has coordinate vector v 2 F 1` and that 2 DF .V / has coordinate vector w 2 F `1 . Then if 0 is the sequence of coordinate vectors of the elements of , we have 0
D fvAi g1 i D0 and
D fvAi wg1 i D0 :
This more concrete, matrix-oriented point of view is sometimes useful; in particular, it makes quite transparent the symmetry of the roles played by ˇ and in forming the projected sequence.
495
18.4 Solving sparse linear systems
E XERCISE 18.7. If jF j D q and 2 F ŒX is monic and factors into monic irreducible polynomials in F ŒX as D 1e1 rer , show that ƒF .1/
D
r Y
.1
q
deg. i /
/1
i D1
r X
q
deg. i /
:
i D1
From this, conclude that the probability that Algorithm MP terminates after just one loop iteration is 1 O.m=q/, where m D deg. /. Thus, if q is very large relative to m, it is highly likely that Algorithm MP terminates after just one iteration of the main loop. 18.4 Solving sparse linear systems Let V be a vector space of finite dimension ` > 0 over a finite field F , and let W V ! V be an F -linear map. The goal of this section is to develop time- and space-efficient algorithms for solving equations of the form . / D ıI
(18.4)
that is, given and ı 2 V , find 2 V satisfying (18.4). The algorithms we develop will have the following properties: they will be probabilistic, and will use an expected number of O.`2 / operations in F , an expected number of O.`/ evaluations of , and space for O.`/ elements of F . By an “evaluation of ,” we mean the computation of .˛/ for a given ˛ 2 V . We shall assume that elements of V are represented as coordinate vectors with respect to some fixed basis for V . Now, if the matrix of with respect to the given basis is sparse, having, say, `1Co.1/ non-zero entries, then the space required to represent is `1Co.1/ elements of F , and the time required to evaluate is `1Co.1/ operations in F . Under these assumptions, our algorithms to solve (18.4) use an expected number of `2Co.1/ operations in F , and space for `1Co.1/ elements of F . This is to be compared with standard Gaussian elimination: even if the original matrix is sparse, during the execution of the algorithm, most of the entries in the matrix may eventually be “filled in” with non-zero field elements, leading to a running time of .`3 / operations in F , and a space requirement of .`2 / elements of F . Thus, the algorithms presented here will be much more efficient than Gaussian elimination when the matrix of is sparse. We hasten to point out that the algorithms presented here may be more efficient than Gaussian elimination in other cases, as well. All that matters is that can be evaluated using o.`2 / operations in F and/or represented using space for o.`2 / elements of F — in either case, we obtain a time and/or space improvement over Gaussian elimination. Indeed, there are applications where the matrix of the linear
496
Linearly generated sequences and applications
map may not be sparse, but nevertheless has special structure that allows it to be represented and evaluated in subquadratic time and/or space. We shall only present algorithms that work in two special, but important, cases: the first case is where is invertible, the second case is where is not invertible, ı D 0, and a non-zero solution
to (18.4) is required (i.e., we are looking for a non-zero element of Ker ). In both cases, the key will be to use Algorithm MP in §18.3 to find the minimal polynomial of the linearly generated sequence i WD f˛i g1 iD0 .˛i WD .ˇ/; i D 0; 1; : : :/;
(18.5)
where ˇ is a suitably chosen element of V . From the discussion in Example 18.3, this sequence has full rank, and so we may use Algorithm MP. We may use M WD ` as an upper bound on the degree of (assuming we know nothing more about and ˇ that would allow us to use a smaller upper bound). In using Algorithm MP in this application, note that we do not want to store ˛0 ; : : : ; ˛2` 1 — if we did, we would not satisfy our stated space bound. Instead of storing the ˛i ’s in a “warehouse,” we use a “just in time” strategy for computing them, as follows: In the body of the main loop of Algorithm MP, where we calculate the projections zi WD .˛i /; for i D 0 : : : 2` 1, we perform the computation as follows: ˛ ˇ for i 0 to 2` 1 do zi .˛/, ˛ .˛/ In the test at the bottom of the main loop of Algorithm MP, if g D Pk j 2 V using the following Hornerj D0 aj X , we compute WD g ? like scheme: 0 for j k down to 0 do ./ C aj ˇ With this implementation, Algorithm MP uses an expected number of O.`2 / operations in F , an expected number of O.`/ evaluations of , and space for O.`/ elements of F . Of course, the “warehouse” strategy is faster than the “just in time” strategy by a constant factor, but it uses about ` times as much space; thus, for large `, using the “just in time” strategy is a very good time/space trade-off. The invertible case. Now consider the case where is invertible, and we want to solve (18.4) for a given ı 2 V . We may as well assume that ı ¤ 0, since otherwise, D 0 is the unique solution to (18.4). We proceed as follows. First,
18.4 Solving sparse linear systems
497
using Algorithm MP as discussed above, compute the minimal polynomial of the P sequence defined in (18.5), using ˇ WD ı. Let D jmD0 cj Xj , where cm D 1 and m > 0. Then we have c0 ı C c1 .ı/ C C cm m .ı/ D 0:
(18.6)
We claim that c0 ¤ 0. To prove the claim, suppose that c0 D 0. Then applying 1 to (18.6), we would obtain c1 ı C C cm m
1
.ı/ D 0;
which would imply that =X is a generating polynomial for , contradicting the minimality of . That proves the claim. Since c0 ¤ 0, we can apply 1 to (18.6), and solve for D 1 .ı/ as follows:
D
c0 1 .c1 ı C C cm m
1
.ı//:
To actually compute , we use the same “just in time” strategy as was used in the implementation of the computation of g ? in Algorithm MP, which costs O.`2 / operations in F , O.`/ evaluations of , and space for O.`/ elements of F . The non-invertible case. Now consider the case where is not invertible, and we want to find non-zero 2 V such that . / D 0. The idea is this. Suppose we choose an arbitrary, non-zero element ˇ of V , and use Algorithm MP to compute the minimal polynomial of the sequence defined in (18.5), using this value of P ˇ. Let D jmD0 cj Xj , where m > 0 and cm D 1. Then we have c0 ˇ C c1 .ˇ/ C C cm m .ˇ/ D 0:
(18.7)
Let
WD c1 ˇ C C cm m
1
.ˇ/:
We must have ¤ 0, since D 0 would imply that b =Xc is a non-zero generating polynomial for , contradicting the minimality of . If it happens that c0 D 0, then equation (18.7) implies that . / D 0, and we are done. As before, to actually compute , we use the same “just in time” strategy as was used in the implementation of the computation of g ? in Algorithm MP, which costs O.`2 / operations in F , O.`/ evaluations of , and space for O.`/ elements of F . The above approach fails if c0 ¤ 0. However, in this “bad” case, equation (18.7) implies that ˇ D c0 1 . /; in particular, ˇ 2 Im . One way to avoid such a “bad” ˇ is to randomize: as is not surjective, the image of is a subspace of V of dimension strictly less than `, and therefore, a randomly chosen ˇ lies in the image of with probability at most 1=jF j. So a simple technique is to choose repeatedly ˇ at random until we get a “good” ˇ. The overall complexity of the
498
Linearly generated sequences and applications
resulting algorithm will be as required: O.`2 / expected operations in F , O.`/ expected evaluations of , and space for O.`/ elements of F . As a special case of this situation, consider the problem that arose in Chapter 15 in connection with algorithms for computing discrete logarithms and factoring. We had to solve the following problem: given an ` .` 1/ matrix M with entries in a finite field F , containing `1Co.1/ non-zero entries, find non-zero v 2 F 1` such that vM D 0. To solve this problem, we can augment the matrix M , adding an extra column of zeros, to get an ` ` matrix M 0 . Now, let V D F 1` and let be the F -linear map on V that sends 2 V to M 0 . A non-zero solution to the equation . / D 0 will provide us with the solution to our original problem; thus, we can apply the above technique directly, solving this problem using `2Co.1/ expected operations in F , and space for `1Co.1/ elements of F . As a side remark, in this particular application, we can choose a “good” ˇ in the above algorithm without randomization: just choose ˇ WD .0; : : : ; 0; 1/, which is clearly not in the image of . 18.5 Computing minimal polynomials in F ŒX=.f / (II) Let us return to the problem discussed in §17.2: F is a field, f 2 F ŒX is a monic polynomial of degree ` > 0, and E WD F ŒX=.f / D F Œ, where WD ŒXf ; we are given an element ˛ 2 E, and want to compute the minimal polynomial 2 F ŒX of ˛ over F . As discussed in Example 18.2, this problem is equivalent to the problem of computing the minimal polynomial of the sequence i WD f˛i g1 i D0 .˛i WD ˛ ; i D 0; 1; : : :/;
and the sequence has full rank; therefore, we can use Algorithm MP in §18.3 directly to solve this problem, assuming F is a finite field. If we use the “just in time” strategy in the implementation of Algorithm MP, as was used in §18.4, we get an algorithm that computes the minimal polynomial of ˛ using O.`3 / expected operations in F , but space for just O.`2 / elements of F . Thus, in terms of space, this approach is far superior to the algorithm in §17.2, based on Gaussian elimination. In terms of time complexity, the algorithm based on linearly generated sequences is a bit slower than the one based on Gaussian elimination (but only by a constant factor). However, if we use any subquadratictime algorithm for polynomial arithmetic (see §17.6 and §17.7), we immediately get an algorithm that runs in subcubic time, while still using linear space. In the exercises below, you are asked to develop an algorithm that computes the minimal polynomial of ˛ using just O.`2:5 / operations in F , at the expense of requiring space for O.`1:5 / elements of F — this algorithm does not rely on fast polynomial arithmetic, and can be made even faster if such arithmetic is used.
18.6 The algebra of linear transformations ./
499
E XERCISE 18.8. Let f 2 F ŒX be a monic polynomial of degree ` > 0 over a field F , and let E WD F ŒX=.f /. Also, let WD ŒXf 2 E. For computational purposes, we assume that elements of E and DF .E/ are represented as coordinate vectors with respect to the usual “polynomial” basis f i 1 g`iD1 . For ˇ 2 E, let Mˇ denote the ˇ-multiplication map on E that sends ˛ 2 E to ˛ˇ 2 E, which is an F -linear map from E into E. (a) Show how to compute — given as input the polynomial f defining E, along with a projection 2 DF .E/ and an element ˇ 2 E — the projection B Mˇ 2 DF .E/, using O.`2 / operations in F . (b) Show how to compute — given as input the polynomial f defining E, along with a projection 2 DF .E/, an element ˛ 2 E, and a parameter k > 0— all of the k values .1/; .˛/; : : : ; .˛ k
1
/
using just O.k` C k 1=2 `2 / operations in F , and space for O.k 1=2 `/ elements of F . Hint: use the same hint as in Exercise 17.3. E XERCISE 18.9. Let f 2 F ŒX be a monic polynomial over a finite field F of degree ` > 0, and let E WD F ŒX=.f /. Show how to use the result of the previous exercise, as well as Exercise 17.3, to get an algorithm that computes the minimal polynomial of ˛ 2 E over F using O.`2:5 / expected operations in F , and space for O.`1:5 / operations in F . E XERCISE 18.10. Let f 2 F ŒX be a monic polynomial of degree ` > 0 over a field F (not necessarily finite), and let E WD F ŒX=.f /. Further, suppose that f is irreducible, so that E is itself a field. Show how to compute the minimal polynomial of ˛ 2 E over F deterministically, satisfying the following complexity bounds: (a) O.`3 / operations in F and space for O.`/ elements of F ; (b) O.`2:5 / operations in F and space for O.`1:5 / elements of F . 18.6 The algebra of linear transformations ./ Throughout this chapter, one could hear the whispers of the algebra of linear transformations. We develop some of the aspects of this theory here, leaving a number of details as exercises. It will not play a role in any material that follows, but it serves to provide the reader with a “bigger picture.” Let F be a field and V be a non-trivial F -vector space. We denote by LF .V / the set of all F -linear maps from V into V . Thus, LF .V / D HomF .V; V /, and is
500
Linearly generated sequences and applications
a vector space over F , with addition and scalar multiplication defined point-wise (see Theorem 13.12). Elements of LF .V / are called linear transformations. For ; 0 2 LF .V /, the composed map, B 0 that sends ˛ 2 V to . 0 .˛// is also an element of LF .V /. As always, function composition is associative (i.e., for ; 0 ; 00 2 LF .V /, we have B . 0 B 00 / D . B 0 / B 00 ); however, function composition is not in general commutative (i.e., we may have B 0 ¤ 0 B for some ; 0 2 LF .V /). For 2 LF .V / and an integer i 0, the map i (i.e., the i-fold composition of ) is also an element of LF .V /. Note that for 2 LF .V /, the map 0 is by definition just the identity map on V . For a given linear transformation 2 LF .V / and a given polynomial g 2 F ŒX, P with g D i ai Xi , we denote by g. / the linear transformation X g. / WD ai i : i
Thus, for ˛ 2 V , the value of g. / at ˛ is i ai i .˛/. The following theorem states some simple algebraic properties: P
Theorem 18.6. For all ; 0 ; 00 2 LF .V /, for all c 2 F , and all g; h 2 F ŒX, we have: (a) B . 0 C 00 / D B 0 C B 00 ; (b) . 0 C 00 / B D 0 B C 00 B ; (c) c. B 0 / D .c / B 0 D B .c 0 /; (d) g. / B h. / D .gh/. / D h. / B g. /; (e) g. / C h. / D .g C h/. /. Proof. Exercise. Under the addition operation of the vector space LF .V /, and defining multiplication on LF .V / using the “B” operation, we get an algebraic structure that satisfies all the properties of Definition 7.1, with the exception of property (v) of that definition, namely, commutativity. Thus, we can view LF .V / as a non-commutative ring with unity (the identity map acts as the multiplicative identity). For a fixed 2 LF .V /, we may consider the subset of LF .V /, F Œ WD fg. / W g 2 F ŒXg; which does in fact satisfy all the properties of Definition 7.1. Moreover, we can view F as a subring of F Œ by identifying c 2 F with c 0 2 F Œ . Thus, we may view F Œ as an F -algebra via inclusion. With this convention, for g 2 F ŒX, the expression g. / has its usual meaning as the value of g evaluated at the point in the extension ring F Œ of F .
18.6 The algebra of linear transformations ./
501
Let be the minimal polynomial of over F , so that F Œ is isomorphic as an F -algebra to F ŒX=. /. We can also characterize as follows (verify): if there exists a non-zero polynomial g 2 F ŒX such that g. / D 0, then is the monic polynomial of least degree with this property; otherwise, D 0. Another way to characterize is as follows (verify): is the minimal polynomial of the sequence f i g1 i D0 . Note that is never 1— this follows from the assumption that V is non-trivial. If V has finite dimension `, then by Theorem 14.4, LF .V / is isomorphic as an F -vector space to F `` , and so in particular, has dimension `2 . Therefore, there 2 must be a linear dependence among 1; ; : : : ; ` , which implies that the minimal polynomial of is non-zero with degree at most `2 . We shall show below that in this case, the minimal polynomial of actually has degree at most `. For a fixed 2 LF .V /, we can define a “scalar multiplication” operation ˇ, that maps g 2 F ŒX and ˛ 2 V to g ˇ ˛ WD g. /.˛/ 2 V I that is, if g D
P
i
ai Xi , then gˇ˛ D
X
ai i .˛/:
i
Theorem 18.7. The scalar multiplication ˇ, together with the usual addition operation on V , makes V into an F ŒX-module; that is, for all g; h 2 F ŒX and ˛; ˇ 2 V , we have g ˇ .h ˇ ˛/ D .gh/ ˇ ˛; .g C h/ ˇ ˛ D g ˇ ˛ C h ˇ ˛; g ˇ .˛ C ˇ/ D g ˇ ˛ C g ˇ ˇ; 1 ˇ ˛ D ˛: Proof. Exercise. Note that each choice of gives rise to a different F ŒX-module structure, but all of these structures are extensions of the usual vector space structure, in the sense that for all c 2 F and ˛ 2 V , we have c ˇ ˛ D c˛. Now, for fixed 2 LF .V / and ˛ 2 V , consider the F ŒX-linear map ;˛ W F ŒX ! V that sends g 2 F ŒX to g ˇ ˛ D g. /.˛/. The kernel of this map must be a submodule, and hence an ideal, of F ŒX; since every ideal of F ŒX is principal, it follows that Ker ;˛ is the ideal of F ŒX generated by some polynomial ;˛ , which we can make unique by insisting that it is monic or zero. We call ;˛ the minimal polynomial of ˛ under . We can also characterize ;˛ as follows (verify):
502
Linearly generated sequences and applications
if there exists a non-zero polynomial g 2 F ŒX such that g. /.˛/ D 0, then ;˛ the monic polynomial of least degree with this property; otherwise, ;˛ D 0. Another way to characterize ;˛ is as follows (verify): ;˛ is the minimal polynomial of the sequence f i .˛/g1 i D0 . Note that since . / is the zero map, we have ˇ ˛ D . /.˛/ D 0; and hence 2 Ker ;˛ , which means that ;˛ j . Now consider the image of ;˛ , which we shall denote by h˛i . As an F ŒXmodule, h˛i is isomorphic to F ŒX=. ;˛ /. In particular, if ;˛ is non-zero and has degree m, then h˛i is a vector space of dimension m over F ; indeed, the elements ˛; .˛/; : : : ; m 1 .˛/ form a basis for h˛i over F ; moreover, m is the smallest non-negative integer such that f i .˛/gm i D0 is linearly dependent. Observe that for every ˇ 2 h˛i , we have ;˛ ˇ ˇ D 0; indeed, if ˇ D g ˇ ˛, then ;˛ ˇ .g ˇ ˛/ D . ;˛ g/ ˇ ˛ D g ˇ . ;˛ ˇ ˛/ D g ˇ 0 D 0: The following three theorems develop some simple facts; the proofs of these are straightforward, and left as exercises. In each theorem, is an element of LF .V /, and ˇ is the associated scalar multiplication that makes V into an F ŒX-module. Theorem 18.8. Let ˛ 2 V have minimal polynomial f 2 F ŒX under , and let ˇ 2 V have minimal polynomial g 2 F ŒX under . If gcd.f; g/ D 1, then h˛i \ hˇi D f0g, and ˛ C ˇ has minimal polynomial f g under . Theorem 18.9. Let ˛ 2 V . Let f 2 F ŒX be a monic irreducible polynomial such that f e ˇ ˛ D 0 but f e 1 ˇ ˛ ¤ 0 for some integer e 1. Then f e is the minimal polynomial of ˛ under . Theorem 18.10. Let ˛ 2 V , and suppose that ˛ has minimal polynomial f 2 F ŒX under , with f ¤ 0. Let g 2 F ŒX. Then g ˇ ˛ has minimal polynomial f = gcd.f; g/ under . We are now ready to state the main result of this section, whose statement and proof are analogous to that of Theorem 6.40: Theorem 18.11. Let 2 LF .V /, and suppose that has non-zero minimal polynomial . Then there exists ˇ 2 V such that the minimal polynomial of ˇ under is .
18.6 The algebra of linear transformations ./
503
Proof. Let ˇ be the scalar multiplication associated with . Let D 1e1 rer be the factorization of into monic irreducible polynomials in F ŒX. First, we claim that for each i D 1; : : : ; r, there exists ˛i 2 V such that = i ˇ ˛i ¤ 0. Suppose the claim were false: then for some i , we would have = i ˇ ˛ D 0 for all ˛ 2 V ; however, this means that . = i /. / D 0, contradicting the minimality property in the definition of the minimal polynomial . That proves the claim. Let ˛1 ; : : : ; ˛r be as in the above claim. Then by Theorem 18.9, each = iei ˇ˛i has minimal polynomial iei under . Finally, by Theorem 18.8, ˇ WD = 1e1 ˇ ˛1 C C = rer ˇ ˛r has minimal polynomial under . Theorem 18.11 says that if has minimal polynomial of degree m 0, then 1 there exists ˇ 2 V such that f i .ˇ/gm i D0 is linearly independent. From this, it immediately follows that: Theorem 18.12. If V has finite dimension ` > 0, then for every 2 LF .V /, the minimal polynomial of is non-zero of degree at most `. We close this section a simple observation. Let V be an arbitrary, non-trivial F ŒX-module with scalar multiplication ˇ. Restricting the scalar multiplication from F ŒX to F , we can naturally view V as an F -vector space. Let W V ! V be the map that sends ˛ 2 V to X ˇ ˛. It is easy to see that 2 LF .V /, and that for all polynomials g 2 F ŒX, and all ˛ 2 V , we have g ˇ ˛ D g. /.˛/. Thus, instead of starting with a vector space and defining an F ŒX-module structure in terms of a given linear map, we can go the other direction, starting from an F ŒX-module and obtaining a corresponding linear map. Furthermore, using the language introduced in Examples 13.19 and 13.20, we see that the F ŒX-exponent of V is the ideal of F ŒX generated by the minimal polynomial of , and the F ŒX-order of any element ˛ 2 V is the ideal of F ŒX generated by the minimal polynomial of ˛ under . Theorem 18.11 says that there exists an element in V whose F ŒX-order is equal to the F ŒX-exponent of V , assuming the latter is non-zero. So depending on one’s mood, one can place emphasis either on the linear map , or just talk about F ŒX-modules without mentioning any linear maps. E XERCISE 18.11. Let 2 LF .V / have non-zero minimal polynomial of degree m, and let D 1e1 rer be the factorization of into monic irreducible polynomials in F ŒX. Let ˇ be the scalar multiplication associated with . Show that ˇ 2 V has minimal polynomial under if and only if = i ˇ ˇ ¤ 0 for i D 1; : : : ; r.
504
Linearly generated sequences and applications
E XERCISE 18.12. Let 2 LF .V / have non-zero minimal polynomial . Show that is an invertible map if and only if X − . E XERCISE 18.13. Let F be a finite field, and let V have finite dimension ` > 0 over F . Let 2 LF .V / have minimal polynomial , with deg. / D m (and of course, by Theorem 18.12, we have m `). Suppose that ˛1 ; : : : ; ˛s are randomly chosen elements of V . Let gj be the minimal polynomial of ˛j under , for j D 1; : : : ; s. Let Q be the probability that lcm.g1 ; : : : ; gs / D . The goal of this exercise is to show that Q ƒF .s/, where ƒF .s/ is as defined in §18.3. (a) Using Theorem 18.11 and Theorem 18.10, show that if m D `, then Q D ƒF .s/. (b) Without the assumption that m D `, things are a bit more challenging. Adopting the matrix-oriented point of view discussed at the end of §18.3, and transposing everything, show that – there exists 2 DF .V / such that the sequence f B i g1 i D0 has minimal polynomial , and – if, for j D 1; : : : ; s, we define hj to be the minimal polynomial of the sequence f. i .˛j //g1 i D0 , then the probability that lcm.h1 ; : : : ; hs / D is equal to ƒF .s/.
(c) Show that hj j gj , for j D 1; : : : ; s, and conclude that Q ƒF .s/. E XERCISE 18.14. Let f; g 2 F ŒX with f ¤ 0, and let h WD f = gcd.f; g/. Show that g F ŒX=.f / and F ŒX=.h/ are isomorphic as F ŒX-modules. E XERCISE 18.15. In this exercise, you are to derive the fundamental theorem of finite dimensional F ŒX-modules, which is completely analogous to the fundamental theorem of finite abelian groups. Both of these results are really special cases of a more general decomposition theorem for modules over a principal ideal domain. Let V be an F ŒX-module. Assume that as an F -vector space, V has finite dimension ` > 0, and that the F ŒX-exponent of V is generated by the monic polynomial 2 F ŒX (note that 1 deg. / `). Show that there exist monic, non-constant polynomials 1 ; : : : ; t 2 F ŒX such that i j i C1 for i D 1; : : : ; t
1, and
V is isomorphic, as an F ŒX-module, to the direct product of F ŒX-modules V 0 WD F ŒX=. 1 / F ŒX=. t /: Moreover, show that the polynomials 1 ; : : : ; t satisfying these conditions are uniquely determined, and that t D . Hint: one can just mimic the proof of Theorem 6.44, where the exponent of a group corresponds to the F ŒX-exponent of
18.6 The algebra of linear transformations ./
505
an F ŒX-module, and the order of a group element corresponds to the F ŒX-order of an element of an F ŒX-module — everything translates rather directly, with just a few minor, technical differences, and the previous exercise is useful in proving the uniqueness part of the theorem. E XERCISE 18.16. Let us adopt the same assumptions and notation as in Exercise 18.15, and let 2 LF .V / be the map that sends ˛ 2 V to X ˇ ˛. Further, let W V ! V 0 be the isomorphism of that exercise, and let 0 2 LF .V 0 / be the X-multiplication map on V 0 . (a) Show that B D 0 B . (b) From part (a), derive the following: there exists a basis for V over F , with respect to which the matrix of is the “block diagonal” matrix
˙C
1
C2
T D
::
;
: Ct
where each Ci is the companion matrix of i (see Example 14.1). E XERCISE 18.17. Let us adopt the same assumptions and notation as in Exercise 18.15. (a) Using the result of that exercise, show that V is isomorphic, as an F ŒXmodule, to a direct product of F ŒX-modules F ŒX=.f1e1 / F ŒX=.frer /; where the fi ’s are monic irreducible polynomials (not necessarily distinct) and the ei ’s are positive integers, and this direct product is unique up to the order of the factors. (b) Using part (a), show that there exists a basis for V over F , with respect to which the matrix of is the “block diagonal” matrix
˙C
0 1
0
T D
C20
::
;
: Cr0
where each Ci0 is the companion matrix of fiei . E XERCISE 18.18. Let us adopt the same assumptions and notation as in Exercise 18.15.
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Linearly generated sequences and applications
(a) Suppose ˛ 2 V corresponds to .Œg1 1 ; : : : ; Œg t t / 2 V 0 under the isomorphism of that exercise. Show that the F ŒX-order of ˛ is generated by the polynomial lcm. 1 = gcd.g1 ; 1 /; : : : ; t = gcd.g t ; t //: (b) Using part (a), give a short and simple proof of the result of Exercise 18.13. 18.7 Notes Berlekamp [15] and Massey [62] discuss an algorithm for finding the minimal polynomial of a linearly generated sequence that is closely related to the one presented in §18.2, and which has a similar complexity. This connection between Euclid’s algorithm and finding minimal polynomials of linearly generated sequences has been observed by many authors, including Mills [66], Welch and Scholtz [106], and Dornstetter [36]. The algorithm presented in §18.3, is due to Wiedemann [107], as are the algorithms for solving sparse linear systems in §18.4, as well as the statement and proof outline of the result in Exercise 18.13. Our proof of Theorem 18.4 is based on an exposition by Morrison [67]. Using fast matrix and polynomial arithmetic, Shoup [94] shows how to implement the algorithms in §18.5 so as to use just O.`.!C1/=2 / operations in F , where ! is the exponent for matrix multiplication (see §14.6), and so .! C 1/=2 < 1:7.
19 Finite fields
This chapter develops some of the basic theory of finite fields. As we already know (see Theorem 7.7), every finite field must be of cardinality p w , for some prime p and positive integer w. The main results of this chapter are: for every prime p and positive integer w, there exists a finite field of cardinality p w , and any two finite fields of the same cardinality are isomorphic. 19.1 Preliminaries We begin by stating some simple but useful divisibility criteria for polynomials over an arbitrary field. These will play a crucial role in the development of the theory. Let F be a field. A polynomial f 2 F ŒX is called square-free if it is not divisible by the square of any polynomial of degree greater than zero. Using formal derivatives (see §16.7), we obtain the following useful criterion for establishing that a polynomial is square-free: Theorem 19.1. If F is a field, and f 2 F ŒX with gcd.f; D.f // D 1, then f is square-free. Proof. Suppose f is not square-free, and write f D g 2 h, for g; h 2 F ŒX with deg.g/ > 0. Taking formal derivatives, we have D.f / D 2gD.g/h C g 2 D.h/; and so clearly, g is a common divisor of f and D.f /. Theorem 19.2. Let F be a field, and let k; ` be positive integers. Then Xk divides X` 1 in F ŒX if and only if k divides `.
507
1
508
Finite fields
Proof. Let ` D kq C r, with 0 r < k. We have X` Xkq Xr Xr .mod Xk and Xr 1 .mod Xk
1/;
1/ if and only if r D 0.
Theorem 19.3. Let a 2 be an integer and let k; ` be positive integers. Then ak 1 divides a` 1 if and only if k divides `. Proof. The proof is analogous to that of Theorem 19.2. We leave the details to the reader. One may combine these last two theorems, obtaining: Theorem 19.4. Let a 2 be an integer, k; ` be positive integers, and F a field. k ` Then Xa X divides Xa X in F ŒX if and only if k divides `. k
`
k
`
Proof. Now, Xa X divides Xa X if and only if Xa 1 1 divides Xa 1 1. By Theorem 19.2, this happens if and only if ak 1 divides a` 1. By Theorem 19.3, this happens if and only if k divides `. We end this section by recalling some concepts discussed earlier, mainly in §16.1, §16.5, and §16.6, that will play an important role in this chapter. Suppose F is a field, and E is an extension field of F ; that is, F is a subfield of E (or, more generally, F is embedded in E via some canonical embedding, and we identify elements of F with their images in E under this embedding). We may view E as an F -algebra via inclusion, and in particular, as an F -vector space. If E 0 is also an extension field of F , and W E ! E 0 is a ring homomorphism, then is an F -algebra homomorphism if and only if .a/ D a for all a 2 F . Let us further assume that as an F -vector space, E has finite dimension `. This dimension ` is called the degree of E over F , and is denoted .E W F /, and E is called a finite extension of F . Now consider an element ˛ 2 E. Then ˛ is algebraic over F , which means that there exists a non-zero polynomial g 2 F ŒX such that g.˛/ D 0. The monic polynomial 2 F ŒX of least degree such that .˛/ D 0 is called the minimal polynomial of ˛ over F . The polynomial is irreducible over F , and its degree m WD deg. / is called the degree of ˛ over F . The ring F Œ˛ D fg.˛/ W g 2 F ŒXg, which is the smallest subring of E containg F and ˛, is actually a field, and is isomorphic, as an F -algebra, to F ŒX=. /, via the map that sends g.˛/ 2 F Œ˛ to Œg 2 F ŒX=. /. In particular, .F Œ˛ W F / D m, and the elements 1; ˛; : : : ; ˛ m 1 form a basis for F Œ˛ over F . Moreover, m divides `.
19.2 The existence of finite fields
509
19.2 The existence of finite fields Let F be a finite field. As we saw in Theorem 7.7, F must have cardinality p w , where p is prime and w is a positive integer, and p is the characteristic of F . However, we can say a bit more than this. As discussed in Example 7.53, the field Zp is embedded in F , and so we may simply view Zp as a subfield of F . Moreover, it must be the case that w is equal to .F W Zp /. We want to show that there exist finite fields of every prime-power cardinality. Actually, we shall prove a more general result: If F is a finite field, then for every integer ` 1, there exists an extension field E of degree ` over F . For the remainder of this section, F denotes a finite field of cardinality q D p w , where p is prime and w 1. Suppose for the moment that E is an extension of degree ` over F . Let us derive some basic facts about E. First, observe that E has cardinality q ` . By Theorem 7.29, E is cyclic, and the order of E is q ` 1. If 2 E is a generator for E , then every non-zero element of E can be expressed as a power of ; in particular, every element of E can be expressed as a polynomial in with coefficients in F ; that is, E D F Œ . Let 2 F ŒX be the minimal polynomial of over F , which is an irreducible polynomial of degree `. It follows that E is isomorphic (as an F -algebra) to F ŒX=. /. So we have shown that every extension of degree ` over F must be isomorphic, as an F -algebra, to F ŒX=.f / for some irreducible polynomial f 2 F ŒX of degree `. Conversely, given any irreducible polynomial f over F of degree `, we can construct the finite field F ŒX=.f /, which has degree ` over F . Thus, the question of the existence of a finite field of degree ` over F reduces to the question of the existence of an irreducible polynomial over F of degree `. We begin with a simple generalization Fermat’s little theorem: Theorem 19.5. For every a 2 F , we have aq D a. Proof. The multiplicative group of units F of F has order q 1, and hence, every a 2 F satisfies the equation aq 1 D 1. Multiplying this equation by a yields aq D a for all a 2 F , and this latter equation obviously holds for a D 0 as well. This simple fact has a number of consequences. Theorem 19.6. We have Xq
XD
Y a2F
.X
a/:
510
Finite fields
Proof. Since each a 2 F is a root of Xq X, by Theorem 7.13, the polynomial Q q X. Since the degrees and leading coeffia2F .X a/ divides the polynomial X cients of these two polynomials are the same, the two polynomials must be equal. Theorem 19.7. Let E be an F -algebra. Then the map W E ! E that sends ˛ 2 E to ˛ q is an F -algebra homomorphism. Proof. By Theorem 16.3, either E is trivial or contains an isomorphic copy of F as a subring. In the former case, there is nothing to prove. So assume that E contains an isomorphic copy of F as a subring. It follows that E must have characteristic p. Since q D p w , we see that D w , where .˛/ WD ˛ p . By the discussion in Example 7.48, the map is a ring homomorphism, and hence so is . Moreover, by Theorem 19.5, we have .c˛/ D .c˛/q D c q ˛ q D c˛ q D c.˛/ for all c 2 F and ˛ 2 E. Thus, is also an F -linear map, and hence, an F -algebra homomorphism. Theorem 19.8. Let E be a finite extension of F . Consider the map W E ! E that sends ˛ 2 E to ˛ q 2 E. Then is an F -algebra automorphism on E. Moreover, for all ˛ 2 E, we have .˛/ D ˛ if and only if ˛ 2 F . Proof. The fact that is an F -algebra homomorphism follows from the previous theorem. Any ring homomorphism from a field into a field is injective (see Exercise 7.46). Surjectivity follows from injectivity and finiteness. For the second statement, observe that .˛/ D ˛ if and only if ˛ is a root of the polynomial Xq X, and since all q elements of F are already roots, by Theorem 7.14, there can be no other roots. The map defined in Theorem 19.8 is called the Frobenius map on E over F . As it plays a fundamental role in the study of finite fields, let us develop a few simple properties right away. Since the composition of two F -algebra automorphisms is also an F -algebra automorphism, for every i 0, the i -fold composition i that sends ˛ 2 E to i ˛ q is also an F -algebra automorphism. Since is an F -algebra automorphism, the inverse function 1 is also an F -algebra automorphism. Hence, i is an F algebra automorphism for all i 2 Z. If E has degree ` over F , then applying Theorem 19.5 to the field E, we see that ` is the identity map. More generally, we have: Theorem 19.9. Let E be a extension of degree ` over F , and let be the Frobenius
19.2 The existence of finite fields
511
map on E over F . Then for all integers i and j , we have i D j if and only if i j .mod `/. Proof. We may assume i j . We have i
i D j ” i j D 0 ” ˛q Y i j .X ˛/ j .Xq ”
j
˛ D 0 for all ˛ 2 E X/ (by Theorem 7.13)
˛2E
” .Xq
`
” ` j .i
X/ j .Xq
i
j
X/ (by Theorem 19.6, applied to E)
j / (by Theorem 19.4)
” i j .mod `/: From the above theorem, it follows that every power of the Frobenius map can be written uniquely as i for some i D 0; : : : ; ` 1. The following theorem generalizes Theorem 19.6: Theorem 19.10. For k 1, let Pk denote the product of all the monic irreducible polynomials in F ŒX of degree k. For all positive integers `, we have Y ` Xq XD Pk ; kj`
where the product is over all positive divisors k of `. `
Proof. First, we claim that the polynomial Xq X is square-free. This follows ` ` immediately from Theorem 19.1, since D.Xq X/ D q ` Xq 1 1 D 1. Thus, we have reduced the proof to showing that if f is a monic irreducible ` polynomial of degree k, then f divides Xq X if and only if k divides `. So let f be a monic irreducible polynomial of degree k. Let E WD F ŒX=.f / D F Œ, where WD ŒXf 2 E. Observe that E is an extension field of degree k over F . Let be the Frobenius map on E over F . ` First, we claim that f divides Xq X if and only if ` ./ D . Indeed, f is the ` minimal polynomial of over F , and so f divides Xq X if and only if is a root ` ` of Xq X, which is the same as saying q D , or equivalently, ` ./ D . Second, we claim that ` ./ D if and only if ` .˛/ D ˛ for all ˛ 2 E. To see this, first suppose that ` .˛/ D ˛ for all ˛ 2 E Then in particular, this holds for ˛ D . Conversely, suppose that ` ./ D . Every ˛ 2 E can be written as ˛ D g./ for some g 2 F ŒX, and since ` is an F -algebra homomorphism, by Theorem 16.6 we have ` .˛/ D ` .g.// D g. ` .// D g./ D ˛:
512
Finite fields
Finally, we see that ` .˛/ D ˛ for all ˛ 2 E if and only if ` D 0 , which by Theorem 19.9 holds if and only if k j `. For ` 1, let …F .`/ denote the number of monic irreducible polynomials of degree ` in F ŒX. Theorem 19.11. For all ` 1, we have X k…F .k/: q` D
(19.1)
kj`
Proof. Just equate the degrees of both sides of the identity in Theorem 19.10. From Theorem 19.11 it is easy to deduce that …F .`/ > 0 for all `, and in fact, one can prove a density result — essentially a “prime number theorem” for polynomials over finite fields: Theorem 19.12. For all ` 1, we have q` q` …F .`/ ; 2` `
(19.2)
and …F .`/ D
`=2 q` q CO : ` `
(19.3)
Proof. First, since all the terms in the sum on the right hand side of (19.1) are nonnegative, and `…F .`/ is one of these terms, we may deduce that `…F .`/ q ` , which proves the second inequality in (19.2). Since this holds for all `, we have `…F .`/ D q
`
X
k…F .k/ q
X
`
kj` k<`
k
q q
`
kj` k<`
b`=2c X
qk :
kD1
Let us set S.q; `/ WD
b`=2c X kD1
qk D
q q
1
.q b`=2c
1/;
so that `…F .`/ q ` S.q; `/. It is easy to see that S.q; `/ D O.q `=2 /, which proves (19.3). For the first inequality of (19.2), it suffices to show that S.q; `/ q ` =2. One can check this directly for ` 2 f1; 2; 3g (verify), and for ` 4, we have S.q; `/ q `=2C1 q `
1
q ` =2:
We note that the inequalities in (19.2) are tight, in the sense that …F .`/ D q ` =2` when q D 2 and ` D 2, and …F .`/ D q ` when ` D 1. The first inequality in
19.3 The subfield structure and uniqueness of finite fields
513
(19.2) implies not only that …F .`/ > 0, but that the fraction of all monic degree ` polynomials that are irreducible is at least 1=2`, while (19.3) says that this fraction gets arbitrarily close to 1=` as either q or ` are sufficiently large. E XERCISE 19.1. Starting from Theorem 19.11, show that X …F .`/ D ` 1 .k/q `=k ; kj`
where is the Möbius function (see §2.9). E XERCISE 19.2. How many irreducible polynomials of degree 30 over Z2 are there? 19.3 The subfield structure and uniqueness of finite fields We begin with a result that holds for extension fields in general. Theorem 19.13. Let E be an extension of a field F , and let W E ! E be an F -algebra homomorphism. Then the set K WD f˛ 2 E W .˛/ D ˛g is a subfield of E containing F . Proof. By definition, acts as the identity map on F , and so F K. To show that K is a subring of E, it suffices to show that K is closed under addition and multiplication. To show that K is closed under addition, let ˛; ˇ 2 K. Then .˛ C ˇ/ D .˛/ C .ˇ/ D ˛ C ˇ, and hence ˛ C ˇ 2 K. Replacing “C” by “” in the above argument shows that K is closed under multiplication. We conclude that K is a subring of E. To complete the proof that K is a subfield of E, we need to show that if 0 ¤ ˛ 2 K and ˇ 2 E with ˛ˇ D 1, then ˇ 2 K. We have ˛ˇ D 1 D .1/ D .˛ˇ/ D .˛/.ˇ/ D ˛.ˇ/; and hence ˛ˇ D ˛.ˇ/; canceling ˛, we obtain ˇ D .ˇ/, and so ˇ 2 K. The subfield K in the above theorem is called the subfield of E fixed by . Let E be an extension of degree ` over a field F . If K is an intermediate field, that is, a subfield of E containing F , then Theorem 16.22 says that .E W F / D .E W K/.K W F /, and so in particular, the degree of K over F divides `. In the case where F is a finite field, we can say much more about such intermediate fields: Theorem 19.14. Let E be an extension of degree ` over a finite field F . Let be the Frobenius map on E over F . Then the intermediate fields K, with F K E, are in one-to-one correspondence with the divisors k of `, where the divisor k corresponds to the subfield of E fixed by k , which has degree k over F .
514
Finite fields
Proof. Let q be the cardinality of F . Suppose k is a divisor of `. By Theorem 19.6 (applied to E), the polynomial ` q X splits into distinct monic linear factors over E. By Theorem 19.4, the X k ` k X also splits into distinct monic X. Hence, Xq X divides Xq polynomial Xq linear factors over E. This says that the subfield of E fixed by k , which consists k X, has precisely q k elements, and hence is an extension of of the roots of Xq degree k over F . Now let K be an arbitrary intermediate field, and let k be the degree of K over F . As already mentioned, we must have k j `. Also, by Theorem 19.8 (applied with K in place of F ), K is the subfield of E fixed by k . The next theorem shows that up to isomorphism, there is only one finite field of a given cardinality. Theorem 19.15. Let E and E 0 be finite extensions of the same degree over a finite field F . Then E and E 0 are isomorphic as F -algebras. Proof. Let q be the cardinality of F , and let ` be the degree of the extensions. As we have argued before, we have E 0 D F Œ˛ 0 for some ˛ 0 2 E 0 , and so E 0 is isomorphic as an F -algebra to F ŒX=. /, where is the minimal polynomial of ˛ 0 over F . As is an irreducible polynomial of degree `, by Theorem 19.10, Q ` ` divides Xq X, and by Theorem 19.6 (applied to E), Xq X D ˛2E .X ˛/, from which it follows that has a root ˛ 2 E. Since is irreducible, is the minimal polynomial of ˛ over F , and hence F Œ˛ is isomorphic as an F -algebra to F ŒX=. /. Since ˛ has degree ` over F , we must have E D F Œ˛. Thus, E D F Œ˛ Š F ŒX=. / Š F Œ˛ 0 D E 0 . E XERCISE 19.3. This exercise develops an alternative proof for the existence of finite fields — however, it does not yield a density result for irreducible polynomials. Let F be a finite field of cardinality q, and let ` 1 be an integer. Let E be ` a splitting field for the polynomial Xq X 2 F ŒX (see Theorem 16.24), and let be the Frobenius map on E over F . Let K be the subfield of E fixed by ` . Show that K is an extension of F of degree `. E XERCISE 19.4. Let E be an extension of degree ` over a finite field F of cardinality q. Show that at least half the elements of E have degree ` over F , and that the total number of elements of degree ` over F is q ` C O.q `=2 /. E XERCISE 19.5. Let E be a finite extension of a finite field F , and suppose ˛; ˇ 2 E, where ˛ has degree a over F , ˇ has degree b over F , and gcd.a; b/ D 1. Show that ˇ has degree b over F Œ˛, that ˛ has degree a over F Œˇ, and that ˛ C ˇ has
515
19.4 Conjugates, norms and traces
degree ab over F . Hint: consider the subfields F Œ˛, F Œˇ, F Œ˛Œˇ D F Œ˛; ˇ D F ŒˇŒ˛, and F Œ˛ C ˇ, and their degrees over F . 19.4 Conjugates, norms and traces Throughout this section, F denotes a finite field of cardinality q, E denotes an extension of degree ` over F , and denotes the Frobenius map on E over F . Consider an element ˛ 2 E. We say that ˇ 2 E is conjugate to ˛ (over F ) if ˇ D i .˛/ for some i 2 Z. The reader may verify that the “conjugate to” relation is an equivalence relation. We call the equivalence classes of this relation conjugacy classes, and we call the elements of the conjugacy class containing ˛ the conjugates of ˛. Starting with ˛, we can start listing conjugates: ˛; .˛/; 2 .˛/; : : : : As ` is the identity map, this list will eventually start repeating. Let k be the smallest positive integer such that k .˛/ D i .˛/ for some i D 0; : : : ; k 1. It must be the case that i D 0 — otherwise, applying 1 to the equation k .˛/ D i .˛/ would yield k 1 .˛/ D i 1 .˛/, and since 0 i 1 < k 1, this would contradict the minimality of k. Thus, ˛; .˛/; : : : ; k 1 .˛/ are all distinct, and k .˛/ D ˛. Moreover, for every integer i , we have i .˛/ D j .˛/, where j D i mod k. Therefore, the k distinct elements ˛; .˛/; : : : ; k 1 .˛/ are all the conjugates of ˛. Also, i .˛/ D ˛ if and only if k divides i , and since ` .˛/ D ˛, it must be the case that k divides `. In addition, the conjugates of ˛ are powers of ˛, and in particular, they all belong to F Œ˛. With ˛ and k as above, consider the polynomial WD
kY1
.X
i .˛//:
i D0
The coefficients of obviously lie in E, but we claim that in fact, they lie in F . This is easily seen as follows. Extend the domain of definition of from E to EŒX by applying coefficient-wise to polynomials; this yields a ring homomorphism from EŒX into EŒX, which we also denote by (see Example 7.46). Applying to , we obtain . / D
kY1 i D0
since
k .˛/
.X
i .˛// D
kY1
.X
i D0
iC1 .˛// D
kY1
i .˛//;
.X
iD0
D ˛. Thus we see that . / D . Writing D
P
i
ci Xi , it follows
516
Finite fields
that .ci / D ci for all i, and hence by Theorem 19.8, ci 2 F for all i . Hence 2 F ŒX. We further claim that is the minimal polynomial of ˛. To see this, let f 2 F ŒX be any polynomial over F for which ˛ is a root. Then for every integer i , by Theorem 16.6, we have 0 D i .0/ D i .f .˛// D f . i .˛//: Thus, all the conjugates of ˛ are also roots of f , and so divides f . That proves that is the minimal polynomial of ˛. Since is the minimal polynomial of ˛ and deg. / D k, it follows that the number k is none other than the degree of ˛ over F. Let us summarize the above discussion as follows: Theorem 19.16. Let ˛ 2 E be of degree k over F , and let be the minimal polynomial of ˛ over F . Then k is the smallest positive integer such that k .˛/ D ˛, the distinct conjugates of ˛ are ˛; .˛/; : : : ; k 1 .˛/, and factors over E (in fact, over F Œ˛) as D
kY1
.X
i .˛//:
i D0
Another useful way of reasoning about conjugates is as follows. First, if ˛ D 0, then the degree of ˛ over F is 1, and there is nothing more to say, so let us assume that ˛ 2 E . If r is the multiplicative order of ˛, then note that every conjugate i .˛/ also has multiplicative order r — this follows from the fact that for every positive integer s, ˛ s D 1 if and only if . i .˛//s D 1. Also, note that we must have r j jE j D q ` 1, or equivalently, q ` 1 .mod r/. Focusing now on the fact that is the q-power map, we see that the degree k of ˛ is the smallest k k positive integer such that ˛ q D ˛, which holds if and only if ˛ q 1 D 1, which holds if and only if q k 1 .mod r/. Thus, the degree of ˛ over F is simply the multiplicative order of q modulo r. Again, we summarize these observations as a theorem: Theorem 19.17. If ˛ 2 E has multiplicative order r, then the degree of ˛ over F is equal to the multiplicative order of q modulo r. For ˛ 2 E, define the polynomial WD
`Y 1
.X
i .˛//:
i D0
It is easy to see, using the same type of argument as was used to prove Theo-
517
19.4 Conjugates, norms and traces
rem 19.16, that 2 F ŒX, and indeed, that D `=k ; where k is the degree of ˛ over F . The polynomial is called the characteristic polynomial of ˛ (from E to F ). Two functions that are often useful are the “norm” and “trace.” The norm of ˛ (from E to F ) is defined as NE=F .˛/ WD
`Y 1
i .˛/;
i D0
while the trace of ˛ (from E to F ) is defined as TrE=F .˛/ WD
` 1 X
i .˛/:
i D0
It is easy to see that both the norm and trace of ˛ are elements of F , as they are fixed by ; alternatively, one can see this by observing that they appear, possibly with a minus sign, as coefficients of the characteristic polynomial — indeed, the constant term of is equal to . 1/` NE=F .˛/, and the coefficient of X` 1 in is TrE=F .˛/. The following two theorems summarize the most important facts about the norm and trace functions. Theorem 19.18. The function NE=F , restricted to E , is a group homomorphism from E onto F . Proof. We have NE=F .˛/ D
`Y 1
i
˛q D ˛
P`
1 iD0
qi
D ˛ .q
`
1/=.q 1/
:
i D0
Since E is a cyclic group of order q ` 1, the image of the .q ` 1/=.q 1/-power map on E is the unique subgroup of E of order q 1 (see Theorem 6.32). Since F is a subgroup of E of order q 1, it follows that the image of this power map is F . Theorem 19.19. The function TrE=F is an F -linear map from E onto F . Proof. The fact that TrE=F is an F -linear map is a simple consequence of the fact that is an F -linear map. As discussed above, TrE=F maps into F . Since the image of TrE=F is a subspace of F , the image is either f0g or F , and so it suffices
518
Finite fields
to show that TrE=F does not map all of E to zero. But an element ˛ 2 E is in the kernel of TrE=F if and only if ˛ is a root of the polynomial X C Xq C C X q
` 1
;
which has degree q ` 1 . Since E contains q ` elements, not all elements of E can lie in the kernel of TrE=F . Example 19.1. As an application of some of the above theory, let us investigate the factorization of the polynomial Xr 1 over F , a finite field of cardinality q. Let us assume that r > 0 and is relatively prime to q. Let E be a splitting field of Xr 1 (see Theorem 16.24), so that E is a finite extension of F in which Xr 1 splits into linear factors: r Y Xr 1 D .X ˛i /: i D1
Xr
We claim that the roots ˛i of 1 are distinct — this follows from the Theorem 19.1 and the fact that gcd.Xr 1; rXr 1 / D 1. Next, observe that the r roots of Xr 1 in E actually form a subgroup of E , and since E is cyclic, this subgroup must be cyclic as well. So the roots of Xr 1 form a cyclic subgroup of E of order r. Let be a generator for this group. Then all the roots of Xr 1 are contained in F Œ, and so we may as well assume that E D F Œ. Let us compute the degree of over F . By Theorem 19.17, the degree ` of over F is the multiplicative order of q modulo r. Moreover, the .r/ roots of Xr 1 of multiplicative order r are partitioned into .r/=` conjugacy classes, each of size `; indeed, as the reader is urged to verify, these conjugacy classes are in one-to-one correspondence with the cosets of the subgroup of Zr generated by Œqr , where each such coset C Zr corresponds to the conjugacy class f a W Œar 2 C g. More generally, for every s j r, every root of Xr 1 whose multiplicative order is s has degree k over F , where k is the multiplicative order of q modulo s. As above, the .s/ roots of multiplicative order s are partitioned into .s/=k conjugacy classes, which are in one-to-one correspondence with the cosets of the subgroup of Zs generated by Œqs . This tells us exactly how Xr 1 splits into irreducible factors over F . Things are a bit simpler when r is prime, in which case, from the above discussion, we see that .r Y 1/=` Xr 1 D .X 1/ fi ; i D1
519
19.4 Conjugates, norms and traces
where the fi ’s are distinct monic irreducible polynomials, each of degree `, and ` is the multiplicative order of q modulo r. In the above analysis, instead of constructing the field E using Theorem 16.24, one could instead simply construct E as F ŒX=.f /, where f is any irreducible polynomial of degree `, and where ` is the multiplicative order of q modulo r. We know that such a polynomial f exists by Theorem 19.12, and since E has cardinality q ` , and r j .q ` 1/ D jE j, and E is cyclic, we know that E contains an element of multiplicative order r, and each of the r distinct powers 1; ; : : : ; r 1 are roots of Xr 1, and so this E is a splitting field of Xr 1 over F. E XERCISE 19.6. Let E be an extension of degree ` over a finite field F . Show that for a 2 F , we have NE=F .a/ D a` and TrE=F .a/ D `a. E XERCISE 19.7. Let E be a finite extension of a finite field F . Let K be an intermediate field, F K E. Show that for all ˛ 2 E (a) NE=F .˛/ D NK=F .NE=K .˛//, and (b) TrE=F .˛/ D TrK=F .TrE=K .˛//. E XERCISE 19.8. Let F be a finite field, and let f 2 F ŒX be a monic irreducible polynomial of degree `. Let E D F ŒX=.f / D F Œ, where WD ŒXf . (a) Show that 1 X D.f / D TrE=F . j f
1
/X
j
:
j D1
(b) From part (a), deduce that the sequence of elements TrE=F . j
1
/ .j D 1; 2; : : :/
is linearly generated over F with minimal polynomial f . (c) Show that one can always choose a polynomial f so that sequence in part (b) is purely periodic with period q ` 1. E XERCISE 19.9. Let F be a finite field, and f 2 F ŒX a monic irreducible polynomial of degree k over F . Let E be an extension of degree ` over F . Show that over E, f factors as the product of d distinct monic irreducible polynomials, each of degree k=d , where d WD gcd.k; `/. E XERCISE 19.10. Let E be a finite extension of a finite field F of characteristic p. Show that if ˛ 2 E and 0 ¤ a 2 F , and if ˛ and ˛ C a are conjugate over F , then p divides the degree of ˛ over F .
520
Finite fields
E XERCISE 19.11. Let F be a finite field of characteristic p. For a 2 F , consider the polynomial f WD Xp X a 2 F ŒX. (a) Show that if F D Zp and a ¤ 0, then f is irreducible. (b) More generally, show that if TrF =Zp .a/ ¤ 0, then f is irreducible, and otherwise, f splits into distinct monic linear factors over F . E XERCISE 19.12. Let E be a finite extension of a finite field F . Show that every F -algebra automorphism on E must be a power of the Frobenius map on E over F. E XERCISE 19.13. Show that for all primes p, the polynomial X4 C 1 is reducible in Zp ŒX. (Contrast this to the fact that this polynomial is irreducible in QŒX, as discussed in Exercise 16.48.) E XERCISE 19.14. This exercise depends on the concepts and results in §18.6. Let E be an extension of degree ` over a finite field F . Let be the Frobenius map on E over F . (a) Show that the minimal polynomial of over F is X`
1.
(b) Show that there exists ˇ 2 E such that the minimal polynomial of ˇ under is X` 1. (c) Conclude that ˇ; .ˇ/; : : : ; ` 1 .ˇ/ form a basis for E over F . This type of basis is called a normal basis.
20 Algorithms for finite fields
This chapter discusses efficient algorithms for factoring polynomials over finite fields, and related problems, such as testing if a given polynomial is irreducible, and generating an irreducible polynomial of given degree. Throughout this chapter, F denotes a finite field of characteristic p and cardinality q D p w . In addition to performing the usual arithmetic and comparison operations in F , we assume that our algorithms have access to the numbers p, w, and q, and have the ability to generate random elements of F . Generating such a random field element will count as one “operation in F ,” along with the usual arithmetic operations. Of course, the “standard” ways of representing F as either Zp (if w D 1), or as the ring of polynomials modulo an irreducible polynomial over Zp of degree w (if w > 1), satisfy the above requirements, and also allow for the implementation of arithmetic operations in F that take time O.len.q/2 / on a RAM (using simple, quadratic-time arithmetic for polynomials and integers). 20.1 Generating and constructing irreducible polynomials Let f 2 F ŒX be a monic polynomial of degree ` > 0. We develop here an efficient algorithm that determines if f is irreducible. The idea is a simple application of Theorem 19.10. That theorem says that for k every integer k 1, the polynomial Xq X is the product of all monic irreducibles whose degree divides k. Thus, gcd.Xq X; f / is the product of all the distinct linear 2 factors of f . If f has no linear factors, then gcd.Xq X; f / is the product of all the distinct quadratic irreducible factors of f . And so on. Now, if f is not irreducible, it must be divisible by some irreducible polynomial of degree at most `=2, and if g is an irreducible factor of f of minimal degree, say k, then we have k `=2 and k k gcd.Xq X; f / ¤ 1. Conversely, if f is irreducible, then gcd.Xq X; f / D 1 for 521
522
Algorithms for finite fields
all positive integers k up to `=2. So to test if f is irreducible, it suffices to check if k X; f / D 1 for all positive integers k up to `=2 — if so, we may conclude gcd.Xq that f is irreducible, and otherwise, we may conclude that f is not irreducible. k To carry out the computation efficiently, we note that if h Xq .mod f /, then k gcd.h X; f / D gcd.Xq X; f /. The above observations suggest the following algorithm. Algorithm IPT. On input f , where f 2 F ŒX is a monic polynomial of degree ` > 0, determine if f is irreducible as follows: h X mod f for k 1 to b`=2c do h hq mod f if gcd.h X; f / ¤ 1 then return false return true The correctness of Algorithm IPT follows immediately from the above discussion. As for the running time, we have: Theorem 20.1. Algorithm IPT uses O.`3 len.q// operations in F . Proof. Consider an execution of a single iteration of the main loop. The cost of the qth-powering step (using a standard repeated-squaring algorithm) is O.len.q// multiplications modulo f , and so O.`2 len.q// operations in F . The cost of the gcd computation is O.`2 / operations in F . Thus, the cost of a single loop iteration is O.`2 len.q// operations in F , from which it follows that the cost of the entire algorithm is O.`3 len.q// operations in F . Using a standard representation for F , each operation in F takes time O.len.q/2 / on a RAM, and so the running time of Algorithm IPT on a RAM is O.`3 len.q/3 /, which means that it is a polynomial-time algorithm. Let us now consider the related problem of constructing an irreducible polynomial of specified degree ` > 0. To do this, we can simply use the result of Theorem 19.12, which has the following probabilistic interpretation: if we choose a random, monic polynomial f of degree ` over F , then the probability that f is irreducible is at least 1=2`. This suggests the following probabilistic algorithm: Algorithm RIP. On input `, where ` is a positive integer, generate a monic irreducible polynomial f 2 F ŒX of degree ` as follows:
20.1 Generating and constructing irreducible polynomials
523
repeat choose c0 ; : : : ; c` 1 2 F at random P set f X` C i`D01 ci Xi test if f is irreducible using Algorithm IPT until f is irreducible output f Theorem 20.2. Algorithm RIP uses an expected number of O.`4 len.q// operations in F , and its output is uniformly distributed over all monic irreducibles of degree `. Proof. This is a simple application of the generate-and-test paradigm (see Theorem 9.3, and Example 9.10 in particular). Because of Theorem 19.12, the expected number of loop iterations of the above algorithm is O.`/. Since Algorithm IPT uses O.`3 len.q// operations in F , the statement about the running time of Algorithm RIP is immediate. The statement about its output distribution is clear. The expected running-time bound in Theorem 20.2 is actually a bit of an overestimate. The reason is that if we generate a random polynomial of degree `, it is likely to have a small irreducible factor, which will be discovered very quickly by Algorithm IPT. In fact, it is known (see §20.7) that the expected value of the degree of the least degree irreducible factor of a random monic polynomial of degree ` over F is O.len.`//, from which it follows that the expected number of operations in F performed by Algorithm RIP is actually O.`3 len.`/ len.q//. E XERCISE 20.1. Let f 2 F ŒX be a monic polynomial of degree ` > 0. Also, let WD ŒXf 2 E, where E is the F -algebra E WD F ŒX=.f /. m
(a) Show how to compute — given as input ˛ 2 E and q 2 E (for some m integer m > 0) — the value ˛ q 2 E, using just O.`2:5 / operations in F , and space for O.`1:5 / elements of F . Hint: see Theorems 16.6 and 19.7, as well as Exercise 17.3. m
m0
(b) Show how to compute — given as input q 2 E and q 2 E, where mCm0 m and m0 are positive integers — the value q 2 E, using O.`2:5 / 1:5 operations in F , and space for O.` / elements of F . (c) Show how to compute — given as input q 2 E and a positive integer m — m the value q 2 E, using O.`2:5 len.m// operations in F , and space for O.`1:5 / elements of F . Hint: use a repeated-squaring-like algorithm. E XERCISE 20.2. This exercise develops an alternative irreducibility test. (a) Show that a monic polynomial f 2 F ŒX of degree ` > 0 is irreducible if
524
Algorithms for finite fields
and only if X s j `.
q`
X .mod f / and gcd.Xq
`=s
X; f / D 1 for all primes
(b) Using part (a) and the result of the previous exercise, show how to determine if f is irreducible using O.`2:5 len.`/!.`/ C `2 len.q// operations in F , where !.`/ is the number of distinct prime factors of `. (c) Show that the operation count in part (b) can be reduced to O.`2:5 len.`/ len.!.`// C `2 len.q//. Hint: see Exercise 3.39. E XERCISE 20.3. Design and analyze a deterministic algorithm that takes as input a list of irreducible polynomials f1 ; : : : ; fr 2 F ŒX, where `i WD deg.fi / for i D 1; : : : ; r, and assume that f`i griD1 is pairwise relatively prime. Your algorithm Q should output an irreducible polynomial f 2 F ŒX of degree ` WD riD1 `i using O.`3 / operations in F . Hint: use Exercise 19.5. E XERCISE 20.4. Design and analyze a probabilistic algorithm that, given a monic irreducible polynomial f 2 F ŒX of degree ` as input, generates as output a random monic irreducible polynomial g 2 F ŒX of degree ` (i.e., g should be uniformly distributed over all such polynomials), using an expected number of O.`2:5 / operations in F . Hint: use Exercise 18.9 (or alternatively, Exercise 18.10). E XERCISE 20.5. Let f 2 F ŒX be a monic irreducible polynomial of degree `, let E WD F ŒX=.f /, and let WD ŒXf 2 E. Design and analyze a deterministic algorithm that takes as input the polynomial f defining the extension E, and outputs the values sj WD TrE=F . j / 2 F .j D 0; : : : ; `
1/;
using O.`2 / operations in F . Here, TrE=F is the trace from E to F (see §19.4). Show that given an arbitrary ˛ 2 E, along with the values s0 ; : : : ; s` 1 , one can compute TrE=F .˛/ using just O.`/ operations in F . 20.2 Computing minimal polynomials in F ŒX=.f / (III) We consider, for the third and final time, the problem considered in §17.2 and §18.5: f 2 F ŒX is a monic polynomial of degree ` > 0, and E WD F ŒX=.f / D F Œ, where WD ŒXf ; we are given an element ˛ 2 E, and want to compute the minimal polynomial 2 F ŒX of ˛ over F . We develop an alternative algorithm, based on the theory of finite fields. Unlike the algorithms in §17.2 and §18.5, this algorithm only works when F is finite and the polynomial f is irreducible, so that E is also a finite field. From Theorem 19.16, we know that the degree of ˛ over F is the smallest posk itive integer k such that ˛ q D ˛. By successive qth powering, we can determine
20.3 Factoring polynomials: square-free decomposition
525
qk 1
of ˛, using O.k len.q// the degree k and compute the conjugates ˛; ˛ q ; : : : ; ˛ operations in E, and hence O.k`2 len.q// operations in F . Now, we could simply compute the minimal polynomial by directly using the formula kY1 i .Y/ D .Y ˛ q /: (20.1) i D0
This would involve computations with polynomials in the variable Y whose coefficients lie in the extension field E, although at the end of the computation, we would end up with a polynomial all of whose coefficients lie in F . The cost of this approach would be O.k 2 / operations in E, and hence O.k 2 `2 / operations in F . A more efficient approach is the following. Substituting for Y in the identity (20.1), we have ./ D
kY1
.
i
˛ q /:
i D0
Using this formula, we can compute (given the conjugates of ˛) the value ./ 2 E using O.k/ operations in E, and hence O.k`2 / operations in F . Now, ./ is an element of E, and for computational purposes, it is represented as Œgf for some polynomial g 2 F ŒX of degree less than `. Moreover, ./ D Œ f , and hence g .mod f /. In particular, if k < `, then g D ; otherwise, if k D `, then g D f . In either case, we can recover from g with an additional O.`/ operations in F . Thus, given the conjugates of ˛, we can compute using O.k`2 / operations in F . Adding in the cost of computing the conjugates, this gives rise to an algorithm that computes the minimal polynomial of ˛ using O.k`2 len.q// operations in F . In the worst case, then, this algorithm uses O.`3 len.q// operations in F . A reasonably careful implementation needs space for storing a constant number of elements of E, and hence O.`/ elements of F . For very small values of q, the efficiency of this algorithm will be comparable to that of the algorithm in §18.5, but for large q, it will be much less efficient. Thus, this approach does not really yield a better algorithm, but it does serve to illustrate some of the ideas of the theory of finite fields. 20.3 Factoring polynomials: square-free decomposition In the remaining sections of this chapter, we develop efficient algorithms for factoring polynomials over the finite field F . We begin in this section with a simple and efficient preprocessing step. Recall that a polynomial is called square-free if it is not divisible by the square of any polynomial of degree greater than zero. This
526
Algorithms for finite fields
preprocessing algorithm takes the polynomial to be factored, and partially factors it into a product of square-free polynomials. Given this algorithm, we can focus our attention on the problem of factoring square-free polynomials. Let f 2 F ŒX be a monic polynomial of degree ` > 0. Suppose that f is not square-free. According to Theorem 19.1, d WD gcd.f; D.f // ¤ 1, and so we might hope to get a non-trivial factorization of f by computing d ; however, we have to consider the possibility that d D f . Can this happen? The answer is “yes,” but if it does happen that d D f , we can still get a non-trivial factorization of f by other means: Theorem 20.3. Suppose that f 2 F ŒX is a monic polynomial of degree ` > 0, and that gcd.f; D.f // D f . Then f D g.Xp / for some g 2 F ŒX. Moreover, if P g D i ai Xi , then f D hp , where X p.w 1/ hD ai Xi : (20.2) i
Proof. Since deg.D.f // < deg.f / and gcd.f; D.f // D f , we must have D.f / D P P 0. If f D i ci Xi , then D.f / D i i ci Xi 1 . Since this derivative must be zero, it follows that all the coefficients ci with i 6 0 .mod p/ must be zero to begin with. That proves that f D g.Xp / for some g 2 F ŒX. Furthermore, if h is defined as above, then X p X X p .w 1/ i pw p h D ai X D ai Xip D ai .Xp /i D g.Xp / D f: i
i
i
Our goal now is to design an efficient algorithm that takes as input a monic polynomial f 2 F ŒX of degree ` > 0, and outputs a list of pairs ..g1 ; s1 /; : : : ; .g t ; s t //, where each gi 2 F ŒX is monic, non-constant, and square-free, each si is a positive integer, the family of polynomials fgi gti D1 is pairwise relatively prime, and Q f D ti D1 gisi . We call such a list a square-free decomposition of f . There are a number of ways to do this. The algorithm we present is based on the following theorem, which itself is a simple consequence of Theorem 20.3. Theorem 20.4. Let f 2 F ŒX be a monic polynomial of degree ` > 0. Suppose that the factorization of f into irreducibles is f D f1e1 frer . Then Y f D fi : gcd.f; D.f // 1i r ei 60 .mod p/
20.3 Factoring polynomials: square-free decomposition
527
Proof. The theorem can be restated in terms of the following claim: for each i D 1; : : : ; r, we have fiei j D.f / if ei 0 .mod p/, and fiei
1
j D.f / but fiei − D.f / if ei 6 0 .mod p/.
To prove the claim, we take formal derivatives using the usual rule for products, obtaining Y e X e 1 (20.3) ej fj j D.fj / fk k : D.f / D j
k¤j
Consider a fixed index i . Clearly, fiei divides every term in the sum on the righthand side of (20.3), with the possible exception of the term with j D i . In the case where ei 0 .mod p/, the term with j D i vanishes, and that proves the claim in this case. So assume that ei 6 0 .mod p/. By the previous theorem, and the fact that fi is irreducible, and in particular, not the pth power of any polynomial, we see that D.fi / is non-zero, and (of course) has degree strictly less than that of fi . From this, and (again) the fact that fi is irreducible, it follows that the term with j D i is divisible by fiei 1 , but not by fiei , from which the claim follows. This theorem provides the justification for the following square-free decomposition algorithm. Algorithm SFD. On input f , where f 2 F ŒX is a monic polynomial of degree ` > 0, compute a square-free decomposition of f as follows: initialize an empty list L s 1 repeat j 1, g f = gcd.f; D.f // repeat f f =g, h gcd.f; g/, m g= h if m ¤ 1 then append .m; js/ to L g h, j j C1 until g D 1 if f ¤ 1 then // f is a pth power // compute a pth root as in (20.2) f f 1=p , s ps until f D 1 output L Theorem 20.5. Algorithm SFD correctly computes a square-free decomposition of f using O.`2 C `.w 1/ len.p/=p/ operations in F .
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Algorithms for finite fields
Proof. Let f D i fiei be the factorization of the input f into irreducibles. Let S be the set of indices i such that ei 6 0 .mod p/, and let S 0 be the set of indices i such that ei 0 .mod p/. Also, for j 1, let Sj WD fi 2 S W ei j g and SDj WD fi 2 S W ei D j g. Consider the first iteration of the main loop. By Theorem 20.4, the value first Q assigned to g is i 2S fi . It is straightforward to prove by induction on j that at the beginning of the j th iteration of the inner repeat loop, the value assigned Q Q e j C1 Q to g is i 2Sj fi , and the value assigned to f is i 2Sj fi i i2S 0 fiei . Q Moreover, in the j th loop iteration, the value assigned to m is i 2SDj fi . It Q follows that when the repeat loop terminates, the value assigned to f is i2S 0 fiei , Q and the value assigned to L is a square-free decomposition of i 2S fiei ; if f does not equal 1 at this point, then subsequent iterations of the main loop will append to Q L a square-free decomposition of i 2S 0 fiei . That proves the correctness of the algorithm. Now consider its running time. Again, consider just the first iteration of the main loop. The cost of computing f = gcd.f; D.f // is at most C1 `2 operations in F , for some constant C1 . Now consider the cost of the inner repeat loop. It is not hard to see that the cost of the j th iteration of the inner repeat loop is at most X C2 ` deg.fi / Q
i 2Sj
operations in F , for some constant C2 . This follows from the observation in the Q previous paragraph that the value assigned to g is i 2Sj fi , along with our usual cost estimates for division and Euclid’s algorithm. Therefore, the total cost of all iterations of the inner repeat loop is at most X X C2 ` deg.fi / j 1 i 2Sj
operations in F . In this double summation, for each i 2 S , the term deg.fi / is counted exactly ei times, and so we can write this cost estimate as X C2 ` ei deg.fi / C2 `2 : i 2S
Finally, it is easy to see that in the if-then statement at the end of the main loop body, if the algorithm does in fact compute a pth root, then this takes at most C3 `.w
1/ len.p/=p
operations in F , for some constant C3 . Thus, we have shown that the total cost of the first iteration of the main loop is at most .C1 C C2 /`2 C C3 `.w
1/ len.p/=p
20.4 Factoring polynomials: the Cantor–Zassenhaus algorithm
529
operations in F . If the main loop is executed a second time, the degree of f at the start of the second iteration is at most `=p, and hence the cost of the second loop iteration is at most .C1 C C2 /.`=p/2 C C3 .`=p/.w
1/ len.p/=p
operations in F . More generally, for t D 1; 2; : : : ; the cost of loop iteration t is at most .C1 C C2 /.`=p t
1 2
/ C C3 .`=p t
1
/.w
1/ len.p/=p;
operations in F , and summing over all t 1 yields the stated bound. 20.4 Factoring polynomials: the Cantor–Zassenhaus algorithm In this section, we present an algorithm due to Cantor and Zassenhaus for factoring a given polynomial over the finite field F into irreducibles. We shall assume that the input polynomial is square-free, using Algorithm SFD in §20.3 as a preprocessing step, if necessary. The algorithm has two stages: Distinct Degree Factorization: The input polynomial is decomposed into factors so that each factor is a product of distinct irreducibles of the same degree (and the degree of those irreducibles is also determined). Equal Degree Factorization: Each of the factors produced in the distinct degree factorization stage are further factored into their irreducible factors. The algorithm we present for distinct degree factorization is a deterministic, polynomial-time algorithm. The algorithm we present for equal degree factorization is a probabilistic algorithm that runs in expected polynomial time (and whose output is always correct). 20.4.1 Distinct degree factorization The problem, more precisely stated, is this: given a monic, square-free polynomial f 2 F ŒX of degree ` > 0, produce a list of pairs ..g1 ; k1 /; : : : ; .g t ; k t // where each gi is the product of monic irreducible polynomials of degree ki , and Q f D ti D1 gi . This problem can be easily solved using Theorem 19.10, using a simple variation of the algorithm we discussed in §20.1 for irreducibility testing. The basic idea is this. We can compute g WD gcd.Xq X; f /, so that g is the product of all the distinct linear factors of f . After removing all linear factors from f , we next 2 compute gcd.Xq X; f /, which will be the product of all the distinct quadratic 2 irreducibles dividing f , and we can remove these from f — although Xq X is
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Algorithms for finite fields
the product of all linear and quadratic irreducibles, since we have already removed the linear factors from f , the gcd will give us just the quadratic factors of f . In general, for k D 1; : : : ; `, having removed all the irreducible factors of degree less k than k from f , we compute gcd.Xq X; f / to obtain the product of all the distinct irreducible factors of f of degree k, and then remove these from f . The above discussion leads to the following algorithm for distinct degree factorization. Algorithm DDF. On input f , where f 2 F ŒX is a monic square-free polynomial of degree ` > 0, do the following: initialize an empty list L h X mod f k 0 while f ¤ 1 do h hq mod f , k kC1 g gcd.h X; f / if g ¤ 1 then append .g; k/ to L f f =g h h mod f output L The correctness of Algorithm DDF follows from the discussion above. As for the running time: Theorem 20.6. Algorithm DDF uses O.`3 len.q// operations in F . Proof. Note that the body of the main loop is executed at most ` times, since after ` iterations, we will have removed all the factors of f . Thus, we perform at most ` qth-powering steps, each of which takes O.`2 len.q// operations in F , and so the total contribution to the running time of these is O.`3 len.q// operations in F . We also have to take into account the cost of the gcd computations. We perform one gcd computation in every iteration of the main loop, for a total of ` such computations. As each of these takes O.`2 / operations in F , they contribute a term of O.`3 / to the total operation count. This term is dominated by the cost of the qth-powering steps (as are the costs of the division steps in the body of the ifthen statement), and so the total cost of Algorithm DDF is O.`3 len.q// operations in F .
20.4 Factoring polynomials: the Cantor–Zassenhaus algorithm
531
20.4.2 Equal degree factorization The problem, more precisely stated, is this: given a monic polynomial f 2 F ŒX of degree ` > 0, and an integer k > 0, such that f is of the form f D f1 fr for distinct monic irreducible polynomials f1 ; : : : ; fr , each of degree k, compute these irreducible factors of f . Note that given f and k, the value of r is easily determined, since r D `=k. We begin by discussing the basic mathematical ideas that will allow us to efficiently split f into two non-trivial factors, and then we present a somewhat more elaborate algorithm that completely factors f . By the Chinese remainder theorem, we have an F -algebra isomorphism W
E ! E1 Er Œgf 7! .Œgf1 ; : : : ; Œgfr /;
where E is the F -algebra F ŒX=.f /, and for i D 1; : : : ; r, Ei is the extension field F ŒX=.fi / of degree k over F . Recall that q D p w . We have to treat the cases p D 2 and p > 2 separately. We first treat the case p D 2. Let us define the polynomial Mk WD
wk X1
j
X2 2 F ŒX:
(20.4)
j D0
(The algorithm in the case p > 2 will only differ in the definition of Mk .) For ˛ 2 E, if .˛/ D .˛1 ; : : : ; ˛r /, then we have .Mk .˛// D Mk ..˛// D .Mk .˛1 /; : : : ; Mk .˛r //: Note that each Ei is an extension of Z2 of degree wk, and that Mk .˛i / D
wk X1
j
˛i2 D TrEi =Z2 .˛i /;
j D0
where TrEi =Z2 W Ei ! Z2 is the trace from Ei to Z2 , which is a surjective, Z2 -linear map (see §19.4). Now, suppose we choose ˛ 2 E at random. Then if .˛/ D .˛1 ; : : : ; ˛r /, the family of random variables f˛i griD1 is mutually independent, with each ˛i uniformly distributed over Ei . It follows that the family of random variables fMk .˛i /griD1 is mutually independent, with each Mk .˛i / uniformly distributed over Z2 . Thus, if g WD rep.Mk .˛// (i.e., g 2 F ŒX is the polynomial of degree less than ` such that Mk .˛/ D Œgf ), then gcd.g; f / will be the product of those factors
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Algorithms for finite fields
fi of f such that Mk .˛i / D 0. We will fail to get a non-trivial factorization only if the Mk .˛i / are either all 0 or all 1, which for r 2 happens with probability at most 1=2 (the worst case being when r D 2). That is our basic splitting strategy. The algorithm for completely factoring f works as follows. The algorithm proceeds in stages. At any stage, we have a partial Q factorization f D h2H h, where H is a set of non-constant, monic polynomials. Initially, H D ff g. With each stage, we attempt to get a finer factorization of f by trying to split each h 2 H using the above splitting strategy — if we succeed in splitting h into two non-trivial factors, then we replace h by these two factors. We continue in this way until jH j D r. Here is the full equal degree factorization algorithm. Algorithm EDF. On input f; k, where f 2 F ŒX is a monic polynomial of degree ` > 0, and k is a positive integer, such that f is the product of r WD `=k distinct monic irreducible polynomials, each of degree k, do the following, with Mk as defined in (20.4): H ff g while jH j < r do H0 ; for each h 2 H do choose ˛ 2 F ŒX=.h/ at random d gcd.rep.Mk .˛//; h/ if d D 1 or d D h then H 0 H 0 [ fhg else H 0 H 0 [ fd; h=d g 0 H H output H The correctness of the algorithm is clear from the above discussion. As for its expected running time, we can get a quick-and-dirty upper bound as follows: For a given h, the cost of computing Mk .˛/ for ˛ 2 F ŒX=.h/ is O.k deg.h/2 len.q// operations in F , and so the number of operations in F performed in each iteration of the main loop is at most a constant times X 2 X 2 k len.q/ deg.h/ k len.q/ deg.h/ D k`2 len.q/: h2H
h2H
The expected number of iterations of the main loop until we get some nontrivial split is O.1/. The algorithm finishes after getting r
1 non-trivial splits.
20.4 Factoring polynomials: the Cantor–Zassenhaus algorithm
533
Therefore, the total expected cost is O.rk`2 len.q//, or O.`3 len.q//, operations in F . This analysis gives a bit of an over-estimate — it does not take into account the fact that we expect to get fairly “balanced” splits. For the purposes of analyzing the overall running time of the Cantor–Zassenhaus algorithm, this bound suffices; however, the following analysis gives a tight bound on the complexity of Algorithm EDF. Theorem 20.7. In the case p D 2, Algorithm EDF uses an expected number of O.k`2 len.q// operations in F . Proof. We may assume r 2. Let L be the random variable that represents the number of iterations of the main loop of the algorithm. For n 1, let Hn be the random variable that represents the value of H at the beginning of the nth loop iteration. For i; j D 1; : : : ; r, we define Lij to be the largest value of n (with 1 n L) such that fi j h and fj j h for some h 2 Hn . We first claim that EŒL D O.len.r//. To prove this claim, we make use of the fact (see Theorem 8.17) that X PŒL n: EŒL D n1
Now, L n if and only if for some i; j with 1 i < j r, we have Lij n. Moreover, if fi and fj have not been separated at the beginning of one loop iteration, then they will be separated at the beginning of the next with probability 1=2. It follows that PŒLij n D 2
.n 1/
:
So we have PŒL n
X
PŒLij n r 2 2
n
:
i <j
Therefore, EŒL D
X
X
PŒL n D
n1
2 log2 r C
n2 log2 r
X n>2 log2 r
r 22
X
PŒL n C n
PŒL n
n>2 log2 r
2 log2 r C
X
2
n
D 2 log2 r C 2;
n0
which proves the claim. As discussed in the paragraph above this theorem, the cost of each iteration of the main loop is O.k`2 len.q// operations in F . Combining this with the fact that EŒL D O.len.r//, it follows that the expected number of operations in F for the
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Algorithms for finite fields
entire algorithm is O.len.r/k`2 len.q//. This is significantly better than the above quick-and-dirty estimate, but is not quite the result we are after. For this, we have to work a little harder. For each polynomial h dividing f , define !.h/ to be the number of irreducible factors of h. Let us also define the random variable S WD
L X X
!.h/2 :
nD1 h2Hn
It is easy to see that the total number of operations performed by the algorithm is O.Sk 3 len.q//, and so it will suffice to show that EŒS D O.r 2 /. We claim that X SD Lij ; i;j
where the sum is over all i; j D 1; : : : ; r. To see this, define ıij .h/ to be 1 if both fi and fj divide h, and 0 otherwise. Then we have X X X XX X X SD ıij .h/ D ıij .h/ D Lij ; n h2Hn i;j
i;j
n h2Hn
i;j
which proves the claim. We can write SD
X
Lij C
X
Li i D
i
i ¤j
X
Lij C rL:
i ¤j
For i ¤ j , we have EŒLij D
X
PŒLij n D
n1
X
2
.n 1/
D 2;
i 1
and so EŒS D
X
EŒLij C r EŒL D 2r.r
1/ C O.r len.r// D O.r 2 /:
i ¤j
That proves the theorem. That completes the discussion of Algorithm EDF in the case p D 2. Now assume that p > 2, so that p, and hence also q, is odd. Algorithm EDF in this case is exactly the same as above, except that in this case, we define the polynomial Mk as Mk WD X.q
k
1/=2
1 2 F ŒX:
Just as before, for ˛ 2 E with .˛/ D .˛1 ; : : : ; ˛r /, we have .Mk .˛// D Mk ..˛// D .Mk .˛1 /; : : : ; Mk .˛r //:
(20.5)
20.4 Factoring polynomials: the Cantor–Zassenhaus algorithm
535
Note that each group Ei is a cyclic group of order q k 1, and therefore, the image of the .q k 1/=2-power map on Ei is f˙1g. Now, suppose we choose ˛ 2 E at random. Then if .˛/ D .˛1 ; : : : ; ˛r /, the family of random variables f˛i griD1 is mutually independent, with each ˛i uniformly distributed over Ei . It follows that the family of random variables fMk .˛i /griD1 is mutually independent. If ˛i D 0, which happens with proba.q k 1/=2
bility 1=q k , then Mk .˛i / D 1; otherwise, ˛i is uniformly distributed over f˙1g, and so Mk .˛i / is uniformly distributed over f0; 2g. That is to say,
˚
Mk .˛i / D
0 with probability .q k 1/=2q k ; 1 with probability 1=q k ; 2 with probability .q k 1/=2q k :
Thus, if g WD rep.Mk .˛//, then gcd.g; f / will be the product of those factors fi of f such that Mk .˛i / D 0. We will fail to get a non-trivial factorization only if the Mk .˛i / are either all zero or all non-zero. Assume r 2. Consider the worst case, namely, when r D 2. In this case, a simple calculation shows that the probability that we fail to split these two factors is k k q 1 2 1 q C1 2 C D .1 C 1=q 2k /: k k 2 2q 2q The (very) worst case is when q k D 3, in which case the probability of failure is at most 5=9. The same quick-and-dirty analysis given just above Theorem 20.7 applies here as well, but just as before, we can do better: Theorem 20.8. In the case p > 2, Algorithm EDF uses an expected number of O.k`2 len.q// operations in F . Proof. The analysis is essentially the same as in the case p D 2, except that now the probability that we fail to split a given pair of irreducible factors is at most 5=9, rather than equal to 1=2. The details are left as an exercise for the reader. 20.4.3 Analysis of the whole algorithm Given an arbitrary monic square-free polynomial f 2 F ŒX of degree ` > 0, the distinct degree factorization step takes O.`3 len.q// operations in F . This step produces a number of polynomials that must be further subjected to equal degree factorization. If there are t such polynomials, where the i th polynomial has degree P `i , for i D 1; : : : ; t , then ti D1 `i D `. Now, the equal degree factorization step for the i th polynomial takes an expected number of O.`3i len.q// operations in F
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Algorithms for finite fields
(actually, our initial, “quick and dirty” estimate is good enough here), and so it follows that the total expected cost of all the equal degree factorization steps is P O. i `3i len.q//, which is O.`3 len.q//, operations in F . Putting this all together, we conclude: Theorem 20.9. The Cantor–Zassenhaus factoring algorithm uses an expected number of O.`3 len.q// operations in F . This bound is tight, since in the worst case, when the input is irreducible, the algorithm really does do this much work. Also, we have assumed the input to the Cantor–Zassenhaus is a square-free polynomial. However, we may use Algorithm SFD as a preprocessing step to ensure that this is the case. Even if we include the cost of this preprocessing step, the running time estimate in Theorem 20.9 remains valid. E XERCISE 20.6. Show how to modify Algorithm DDF so that the main loop halts as soon as 2k > deg.f /. E XERCISE 20.7. Suppose that in Algorithm EDF, we replace the two lines for each h 2 H do choose ˛ 2 F ŒX=.h/ at random by the following: choose a0 ; : : : ; a2k 1 2 F at random P2k 1 j g j D0 aj X 2 F ŒX for each h 2 H do ˛ Œgh 2 F ŒX=.h/ Show that the expected running time bound of Theorem 20.6 still holds (you may assume p D 2 for simplicity). E XERCISE 20.8. This exercise extends the techniques developed in Exercise 20.1. Let f 2 F ŒX be a monic polynomial of degree ` > 0, and let WD ŒXf 2 E, where E WD F ŒX=.f /. For integer m > 0, define polynomials Tm WD X C Xq C C Xq
m 1
2 F ŒX and Nm WD X Xq Xq m
m0
m 1
2 F ŒX:
(a) Show how to compute — given as input q 2 E and q , where m and m0 are positive integers, along with Tm .˛/ and Tm0 .˛/, for some ˛ 2 E — the mCm0 values q and TmCm0 .˛/, using O.`2:5 / operations in F , and space 1:5 for O.` / elements of F .
20.5 Factoring polynomials: Berlekamp’s algorithm
537
(b) Using part (a), show how to compute — given as input q 2 E, ˛ 2 E, and a positive integer m— the value Tm .˛/, using O.`2:5 len.m// operations in F , and space for O.`1:5 / elements of F . (c) Repeat parts (a) and (b), except with “N ” in place of “T .” E XERCISE 20.9. Using the result of the previous exercise, show how to implement Algorithm EDF so that it uses an expected number of O.len.k/`2:5 C `2 len.q// operations in F , and space for O.`1:5 / elements of F . E XERCISE 20.10. This exercise depends on the concepts and results in §18.6. Let E be an extension field of degree ` over F , specified by an irreducible polynomial of degree ` over F . Design and analyze an efficient probabilistic algorithm that finds a normal basis for E over F (see Exercise 19.14). Hint: there are a number of approaches to solving this problem; one way is to start by factoring X` 1 over F , and then turn the construction in Theorem 18.11 into an efficient probabilistic procedure; if you mimic Exercise 11.2, your entire algorithm should use O.`3 len.`/ len.q// operations in F (or O.len.r/`3 len.q// operations, where r is the number of distinct irreducible factors of X` 1 over F ). 20.5 Factoring polynomials: Berlekamp’s algorithm We now develop an alternative algorithm, due to Berlekamp, for factoring a polynomial over the finite field F into irreducibles. We shall assume that the input polynomial is square-free, using Algorithm SFD in §20.3 as a preprocessing step, if necessary. Let us now assume we have a monic square-free polynomial f of degree ` > 0 that we want to factor into irreducibles. We first present the mathematical ideas underpinning the algorithm. Let E be the F -algebra F ŒX=.f /. We define a subset B of E as follows: B WD f˛ 2 E W ˛ q D ˛g: It is easy to see that B is a subalgebra of E. Indeed, for ˛; ˇ 2 B, we have .˛ C ˇ/q D ˛ q C ˇ q D ˛ C ˇ, and similarly, .˛ˇ/q D ˛ q ˇ q D ˛ˇ. Furthermore, one sees that c q D c for all c 2 F , and hence B is a subalgebra. The subalgebra B is called the Berlekamp subalgebra of E. Let us take a closer look at it. Suppose that f factors into irreducibles as f D f1 fr ;
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Algorithms for finite fields
and let W
E ! E1 Er Œgf 7! .Œgf1 ; : : : ; Œgfr /
be the F -algebra isomorphism from the Chinese remainder theorem, where Ei WD F ŒX=.fi / is an extension field of F of finite degree for i D 1; : : : ; r. Now, for q ˛ 2 E, if .˛/ D .˛1 ; : : : ; ˛r /, then we have ˛ q D ˛ if and only if ˛i D ˛i for i D 1; : : : ; r; moreover, by Theorem 19.8, we know that for all ˛i 2 Ei , we have q ˛i D ˛i if and only if ˛i 2 F . Thus, we may characterize B as follows: B D f
1
.c1 ; : : : ; cr / W c1 ; : : : ; cr 2 F g:
Since B is a subalgebra of E, then as F -vector spaces, B is a subspace of E. Of course, E has dimension ` over F , with the natural basis f i 1 g`iD1 , where WD ŒXf . As for the Berlekamp subalgebra, from the above characterization of B, it is evident that the elements
1
.1; 0; : : : ; 0/;
1
.0; 1; 0; : : : ; 0/; : : : ;
1
.0; : : : ; 0; 1/
form a basis for B over F , and hence, B has dimension r over F . Now we come to the actual factoring algorithm. Stage 1: Construct a basis for B The first stage of Berlekamp’s factoring algorithm constructs a basis for B over F . We can easily do this using Gaussian elimination, as follows. Let W E ! E be the map that sends ˛ 2 E to ˛ q ˛. Since the qth power map on E is an F -algebra homomorphism (see Theorem 19.7) — and in particular, an F -linear map —the map is also F -linear. Moreover, the kernel of is none other than the Berlekamp subalgebra B. So to find a basis for B, we simply need to find a basis for the kernel of using Gaussian elimination over F , as in §14.4. To perform the Gaussian elimination, we need to choose a basis S for E over F , and construct the matrix Q WD MatS;S ./ 2 F `` , that is, the matrix of with respect to this basis, as in §14.2, so that evaluation of corresponds to multiplying a row vector on the right by Q. We are free to choose a basis in any convenient way, and the most convenient basis, of course, is S WD f i 1 g`iD1 , since for computational purposes, we already represent an element ˛ 2 E by its coordinate vector VecS .˛/. The matrix Q, then, is the ` ` matrix whose i th row, for i D 1; : : : ; `, is VecS .. i 1 //. Note that if ˛ D q , then . i 1 / D . i 1 /q i 1 D . q /i 1 i 1 D ˛ i 1 i 1 : This observation allows us to construct the rows of Q by first computing q via repeated squaring, and then just computing successive powers of q .
20.5 Factoring polynomials: Berlekamp’s algorithm
539
After we construct the matrix Q, we apply Gaussian elimination to get row vectors v1 ; : : : ; vr that form a basis for the row null space of Q. It is at this point that our algorithm actually discovers the number r of irreducible factors of f . Our basis for B is fˇi griD1 , where VecS .ˇi / D vi for i D 1; : : : ; r. Putting this altogether, we have the following algorithm to compute a basis for the Berlekamp subalgebra. Algorithm B1. On input f , where f 2 F ŒX is a monic square-free polynomial of degree ` > 0, do the following, where E WD F ŒX=.f /, WD ŒXf 2 E, and S WD f i 1 g`iD1 : let Q be an ` ` matrix over F (initially with undefined entries) compute ˛ q using repeated squaring ˇ 1E for i 1 to ` do // invariant: ˇ D ˛ i 1 D . i 1 /q Rowi .Q/ VecS .ˇ/, Q.i; i / Q.i; i / 1, ˇ ˇ˛ r compute a basis fvi gi D1 of the row null space of Q using Gaussian elimination for i D 1; : : : ; r do ˇi VecS 1 .vi / r output fˇi gi D1 The correctness of Algorithm B1 is clear from the above discussion. As for the running time: Theorem 20.10. Algorithm B1 uses O.`3 C `2 len.q// operations in F . Proof. This is just a matter of counting. The computation of ˛ takes O.len.q// operations in E using repeated squaring, and hence O.`2 len.q// operations in F . To build the matrix Q, we have to perform an additional O.`/ operations in E to compute the successive powers of ˛, which translates into O.`3 / operations in F . Finally, the cost of Gaussian elimination is an additional O.`3 / operations in F . Stage 2: Splitting with a basis for B The second stage of Berlekamp’s factoring algorithm is a probabilistic procedure that factors f using a basis fˇi griD1 for B. As we did with Algorithm EDF in §20.4.2, we begin by discussing how to efficiently split f into two non-trivial factors, and then we present a somewhat more elaborate algorithm that completely factors f . Let M1 2 F ŒX be the polynomial defined by (20.4) and (20.5); that is, ( P w 1 2j if p D 2, j D0 X M1 WD .q 1/=2 X 1 if p > 2.
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Algorithms for finite fields
Using our basis for B, we can easily generate a random element ˇ of B P by simply choosing c1 ; : : : ; cr at random, and computing ˇ WD i ci ˇi . If r .ˇ/ D .b1 ; : : : ; br /; then the family of random variables fbi gi D1 is mutually independent, with each bi uniformly distributed over F . Just as in Algorithm EDF, gcd.rep.M1 .ˇ//; f / will be a non-trivial factor of f with probability at least 1=2, if p D 2, and probability at least 4=9, if p > 2. That is the basic splitting strategy. We turn this into an algorithm to completely factor f using the same technique of iterative refinement that was used in Algorithm EDF. That is, at any stage of the algorithm, we have a partial factorization Q f D h2H h, which we try to refine by attempting to split each h 2 H using the strategy outlined above. One technical difficulty is that to split such a polynomial h, we need to efficiently generate a random element of the Berlekamp subalgebra of F ŒX=.h/. A particularly efficient way to do this is to use our basis for the Berlekamp subalgebra of F ŒX=.f / to generate a random element of the Berlekamp subalgebra of F ŒX=.h/ for all h 2 H simultaneously. Let gi WD rep.ˇi / for i D 1; : : : ; r. If we choose c1 ; : : : ; cr 2 F at random, and set g WD c1 g1 C C cr gr , then Œgf is a random element of the Berlekamp subalgebra of F ŒX=.f /, and by the Chinese remainder theorem, it follows that the family of random variables fŒgh gh2H is mutually independent, with each Œgh uniformly distributed over the Berlekamp subalgebra of F ŒX=.h/. Here is the algorithm for completely factoring a polynomial, given a basis for the corresponding Berlekamp subalgebra. Algorithm B2. On input f; fˇi griD1 , where f 2 F ŒX is a monic square-free polynomial of degree ` > 0, and fˇi griD1 is a basis for the Berlekamp subalgebra of F ŒX=.f /, do the following, where gi WD rep.ˇi / for i D 1; : : : ; r: H ff g while jH j < r do choose c1 ; : : : ; cr 2 F at random g c1 g1 C C cr gr 2 F ŒX H0 ; for each h 2 H do ˇ Œgh 2 F ŒX=.h/ d gcd.rep.M1 .ˇ//; h/ if d D 1 or d D h then H 0 H 0 [ fhg 0 else H H 0 [ fd; h=d g H H0 output H
20.5 Factoring polynomials: Berlekamp’s algorithm
541
The correctness of the algorithm is clear. As for its expected running time, we can get a quick-and-dirty upper bound as follows: The cost of generating g in each loop iteration is O.r`/ operations in F . For a given h, the cost of computing ˇ WD Œgh 2 F ŒX=.h/ is O.` deg.h// operations in F , and the cost of computing M1 .ˇ/ is O.deg.h/2 len.q// operations in F . Therefore, the number of operations in F performed in each iteration of the main loop is at most a constant times X X r` C ` deg.h/ C len.q/ deg.h/2 h2H
h2H 2
2` C len.q/
X
2 deg.h/
D O.`2 len.q//:
h2H
The expected number of iterations of the main loop until we get some nontrivial split is O.1/. The algorithm finishes after getting r
1 non-trivial splits.
Therefore, the total expected cost is O.r`2 len.q// operations in F . A more careful analysis reveals: Theorem 20.11. Algorithm B2 uses an expected number of O.len.r/`2 len.q// operations in F . Proof. The proof follows the same line of reasoning as the analysis of Algorithm EDF. Indeed, using the same argument as was used there, the expected number of iterations of the main loop is O.len.r//. As discussed in the paragraph above this theorem, the cost per loop iteration is O.`2 len.q// operations in F . The theorem follows. The bound in the above theorem is tight (see Exercise 20.11 below): unlike Algorithm EDF, we cannot make the multiplicative factor of len.r/ go away. Putting together Algorithms B1 and B2, we get Berlekamp’s complete factoring algorithm. The running time bound is easily estimated from the results already proved: Theorem 20.12. Berlekamp’s factoring algorithm uses an expected number of O.`3 C `2 len.`/ len.q// operations in F . We have assumed the input to Berlekamp’s algorithm is a square-free polynomial. However, we may use Algorithm SFD as a preprocessing step to ensure that
542
Algorithms for finite fields
this is the case. Even if we include the cost of this preprocessing step, the running time estimate in Theorem 20.12 remains valid. So we see that Berlekamp’s algorithm is in fact faster than the Cantor– Zassenhaus algorithm, whose expected operation count is O.`3 len.q//. The speed advantage of Berlekamp’s algorithm grows as q gets large. The one disadvantage of Berlekamp’s algorithm is space: it requires space for ‚.`2 / elements of F , while the Cantor–Zassenhaus algorithm requires space for only O.`/ elements of F . One can in fact implement the Cantor–Zassenhaus algorithm so that it uses O.`3 C `2 len.q// operations in F , while using space for only O.`1:5 / elements of F —see Exercise 20.13 below. E XERCISE 20.11. Give an example of a family of input polynomials that cause Algorithm B2 to use an expected number of at least .`2 len.`/ len.q// operations in F . Assume that computing M1 .ˇ/ for ˇ 2 F ŒX=.h/ takes .deg.h/2 len.q// operations in F . E XERCISE 20.12. Using the ideas behind Berlekamp’s factoring algorithm, devise a deterministic irreducibility test that, given a monic polynomial of degree ` over F , uses O.`3 C `2 len.q// operations in F . E XERCISE 20.13. This exercise develops a variant of the Cantor–Zassenhaus algorithm that uses O.`3 C `2 len.q// operations in F , while using space for only O.`1:5 / elements of F . By making use the variant of Algorithm EDF discussed in Exercise 20.9, our problem is reduced to that of implementing Algorithm DDF within the stated time and space bounds, assuming that the input polynomial is square-free. (a) For non-negative integers i; j , with i ¤ j , show that the irreducible polyi j nomials in F ŒX that divide Xq Xq are precisely those whose degree divides i j . (b) Let f 2 F ŒX be a monic polynomial of degree ` > 0, and let m D O.`1=2 /. Let WD ŒXf 2 E, where E WD F ŒX=.f /. Show how to compute 2
q; q ; : : : ; q
m 1
m
2 E and q ; q
2m
; : : : ; q
.m 1/m
2E
using O.`3 C `2 len.q// operations in F , and space for O.`1:5 / elements of F . (c) Combine the results of parts (a) and (b) to implement Algorithm DDF on square-free inputs of degree `, so that it uses O.`3 C `2 len.q// operations in F , and space for O.`1:5 / elements of F .
20.6 Deterministic factorization algorithms ./
543
20.6 Deterministic factorization algorithms ./ The algorithms of Cantor and Zassenhaus and of Berlekamp are probabilistic. The exercises below develop a deterministic variant of the Cantor–Zassenhaus algorithm. (One can also develop deterministic variants of Berlekamp’s algorithm, with similar complexity.) This algorithm is only practical for finite fields of small characteristic, and is anyway mainly of theoretical interest, since from a practical perspective, there is nothing wrong with the above probabilistic method. In all of these exercises, we assume that we have access to a basis fi gw i D1 for F as a vector space over Zp . To make the Cantor–Zassenhaus algorithm deterministic, we only need to develop a deterministic variant of Algorithm EDF, as Algorithm DDF is already deterministic. E XERCISE 20.14. Let f D f1 fr , where the fi ’s are distinct monic irreducible polynomials in F ŒX. Assume that r > 1, and let ` WD deg.f /. For this exercise, the degrees of the fi ’s need not be the same. For an intermediate field F 0 , with Zp F 0 F , let us call a set S D f1 ; : : : ; s g, where each u 2 F ŒX with deg.u / < `, a separating set for f over F 0 if the following conditions hold: for i D 1; : : : ; r and u D 1; : : : ; s, there exists cui 2 F 0 such that u cui .mod fi /, and for every pair of distinct indices i; j , with 1 i < j r, there exists u D 1; : : : ; s such that cui ¤ cuj . Show that if S is a separating set for f over Zp , then the following algorithm completely factors f using O.pjS j`2 / operations in F . H ff g for each 2 S do for each a 2 Zp do H0 ; for each h 2 H do d gcd. a; h/ if d D 1 or d D h then H 0 H 0 [ fhg 0 else H H 0 [ fd; h=d g H H0 output H E XERCISE 20.15. Let f be as in the previous exercise. Show that if S is a sepa-
544
Algorithms for finite fields
rating set for f over F , then the set 0
S WD
nwX1
i
.j /p mod f W 1 j w; 2 S
o
i D0
is a separating set for f over Zp . Show how to compute this set using O.jS j`2 len.p/w.w 1// operations in F . E XERCISE 20.16. Let f be as in the previous two exercises, but further suppose that each irreducible factor of f is of the same degree, say k. Let E WD F ŒX=.f / and WD ŒXf 2 E. Define the polynomial 2 EŒY as follows: WD
kY1
.Y
i
q /:
i D0
If D Yk C ˛k with ˛0 ; : : : ; ˛k
1
1Y
k 1
C C ˛0 ;
2 E, show that the set S WD frep.˛i / W 0 i k
1g
is a separating set for f over F , and can be computed deterministically using O.k 2 C k len.q// operations in E, and hence O.k 2 `2 C k`2 len.q// operations in F. E XERCISE 20.17. Put together all of the above pieces, together with Algorithms SFD and DDF, so as to obtain a deterministic algorithm for factoring polynomials over F that runs in time at most p times a polynomial in the size of the input, and make a careful estimate of the running time of your algorithm. E XERCISE 20.18. It is a fact that when our prime p is odd, then for all integers a; b, with a 6 b .mod p/, there exists a non-negative integer i p 1=2 log2 p such that .a C i j p/ ¤ .b C i j p/ (here, “. j /” is the Legendre symbol). Using this fact, design and analyze a deterministic algorithm for factoring polynomials over F that runs in time at most p 1=2 times a polynomial in the size of the input. The following two exercises show that the problem of factoring polynomials over F reduces in deterministic polynomial time to the problem of finding roots of polynomials over Zp . E XERCISE 20.19. Let f be as in Exercise 20.14. Suppose that S D f1 ; : : : ; s g is a separating set for f over Zp , and u 2 F ŒX is the minimal polynomial over F of Œu f 2 F ŒX=.f / for u D 1; : : : ; s. Show that each u is the product of
20.7 Notes
545
linear factors over Zp , and that given S , along with the roots of all the u ’s, we can deterministically factor f using .jS j C `/O.1/ operations in F . Hint: see Exercise 16.9. E XERCISE 20.20. Using the previous exercise, show that the problem of factoring a polynomial over F reduces in deterministic polynomial time to the problem of finding roots of polynomials over Zp . 20.7 Notes The average-case analysis of Algorithm IPT, assuming its input is random, and the application to the analysis of Algorithm RIP, is essentially due to Ben-Or [14]. If one implements Algorithm RIP using fast polynomial arithmetic, one gets an expected cost of O.`2Co.1/ len.q// operations in F . Note that Ben-Or’s analysis is a bit incomplete — see Exercise 32 in Chapter 7 of Bach and Shallit [11] for a complete analysis of Ben-Or’s claims. The asymptotically fastest probabilistic algorithm for constructing an irreducible polynomial over F of given degree ` is due to Shoup [94]. That algorithm uses an expected number of O.`2Co.1/ C `1Co.1/ len.q// operations in F , and in fact does not follow the “generate and test” paradigm of Algorithm RIP, but uses a completely different approach. Exercise 20.2 is based on [94]. As far as deterministic algorithms for constructing irreducible polynomials of given degree over F , the only known methods are efficient when the characteristic p of F is small (see Chistov [26], Semaev [86], and Shoup [92]), or under a generalization of the Riemann hypothesis (see Adleman and Lenstra [4]). Shoup [92] in fact shows that the problem of constructing an irreducible polynomial of given degree over F is deterministic, polynomial-time reducible to the problem of factoring polynomials over F . The algorithm in §20.2 for computing minimal polynomials over finite fields is due to Gordon [42]. The square-free decomposition of a polynomial over a field of characteristic zero can be computed using an algorithm of Yun [109] using O.`1Co.1/ / field operations. Yun’s algorithm can be adapted to work over finite fields as well (see Exercise 14.30 in von zur Gathen and Gerhard [38]). The Cantor–Zassenhaus algorithm was initially developed by Cantor and Zassenhaus [24], although many of the basic ideas can be traced back quite a ways. A straightforward implementation of this algorithm using fast polynomial arithmetic uses an expected number of O.`2Co.1/ len.q// operations in F . Berlekamp’s algorithm was initially developed by Berlekamp [15, 16], but again, the basic ideas go back a long way. A straightforward implementation using fast
546
Algorithms for finite fields
polynomial arithmetic uses an expected number of O.`3 C `1Co.1/ len.q// operations in F ; the term `3 may be replaced by `! , where ! is the exponent of matrix multiplication (see §14.6). There are no known efficient, deterministic algorithms for factoring polynomials over F when the characteristic p of F is large (even under a generalization of the Riemann hypothesis, except in certain special cases). The asymptotically fastest algorithms for factoring polynomials over F are due to von zur Gathen, Kaltofen, and Shoup: the algorithm of von zur Gathen and Shoup [39] uses an expected number of O.`2Co.1/ C `1Co.1/ len.q// operations in F ; the algorithm of Kaltofen and Shoup [52] has a cost that is subquadratic in the degree—it uses an expected number of O.`1:815 len.q/0:407 / operations in F . Exercises 20.1, 20.8, and 20.9 are based on [39]. Although the “fast” algorithms in [39] and [52] are mainly of theoretical interest, a variant in [52], which uses O.`2:5 C `1Co.1/ len.q// operations in F , and space for O.`1:5 / elements of F , has proven to be quite practical (Exercise 20.13 develops some of these ideas; see also Shoup [95]).
21 Deterministic primality testing
For many years, despite much research in the area, there was no known deterministic, polynomial-time algorithm for testing whether a given integer n > 1 is a prime. However, that is no longer the case — the breakthrough algorithm of Agrawal, Kayal, and Saxena, or AKS algorithm for short, is just such an algorithm. Not only is the result itself remarkable, but the algorithm is striking in both its simplicity, and in the fact that the proof of its running time and correctness are completely elementary (though ingenious). We should stress at the outset that although this result is an important theoretical result, as of yet, it has no real practical significance: probabilistic tests, such as the Miller–Rabin test discussed in Chapter 10, are much more efficient, and a practically minded person should not at all bothered by the fact that such algorithms may in theory make a mistake with an incredibly small probability. 21.1 The basic idea The algorithm is based on the following fact: Theorem 21.1. Let n > 1 be an integer. If n is prime, then for all a 2 Zn , we have the following identity in the ring Zn ŒX: .X C a/n D Xn C a:
(21.1)
Conversely, if n is composite, then for all a 2 Zn , the identity (21.1) does not hold. Proof. Note that .X C a/n D Xn C an C
n X1 i D1
! n i n i aX : i
If n is prime, then by Fermat’s little theorem (Theorem 2.14), we have an D a, 547
548
Deterministic primality testing
and by Exercise 1.14, all of the binomial coefficients ni , for i D 1; : : : ; n 1, are divisible by n, and hence their images in the ring Zn vanish. That proves that the identity (21.1) holds when n is prime. Conversely, suppose that n is composite and that a 2 Zn . Consider any prime factor p of n, and suppose n D p k m, where p − m. n k We claim that p − p . To prove the claim, one simply observes that ! n n.n 1/ .n p C 1/ D ; pŠ p and the numerator of this fraction is an integer divisible by p k , but no higher power of p, and the denominator is divisible by p, but no higher power of p. That proves the claim. From the claim, and the fact that a 2 Zn , it follows that the coefficient of Xn p in .X C a/n is not zero, and hence the identity (21.1) does not hold. Of course, Theorem 21.1 does not immediately give rise to an efficient primality test, since just evaluating the left-hand side of the identity (21.1) takes time .n/ in the worst case. The key observation of Agrawal, Kayal, and Saxena is that if (21.1) holds modulo Xr 1 for a suitably chosen value of r, and for sufficiently many a, then n must be prime. To make this idea work, one must show that a suitable r exists that is bounded by a polynomial in len.n/, and that the number of different values of a that must be tested is also bounded by a polynomial in len.n/. 21.2 The algorithm and its analysis The algorithm is shown in Fig. 21.1. A few remarks on implementation are in order: In step 1, we can use the algorithm for perfect-power testing discussed in Exercise 3.31. The search for r in step 2 can just be done by brute-force search; likewise, the determination of the multiplicative order of Œnr 2 Zr can be done by brute force: after verifying that gcd.n; r/ D 1, compute successive powers of n modulo r until we get 1. We want to prove that Algorithm AKS runs in polynomial time and is correct. To prove that it runs in polynomial time, it clearly suffices to prove that there exists an integer r satisfying the condition in step 2 that is bounded by a polynomial in len.n/, since all other computations can be carried out in time .r C len.n//O.1/ . Correctness means that it outputs true if and only if n is prime.
21.2 The algorithm and its analysis
549
On input n, where n is an integer and n > 1, do the following: 1. if n is of the form ab for integers a > 1 and b > 1 then return false 2. find the smallest integer r > 1 such that either gcd.n; r/ > 1 or gcd.n; r/ D 1 and Œnr 2 Zr has multiplicative order > 4 len.n/2 3. if r D n then return true 4. if gcd.n; r/ > 1 then return false 5. for j 1 to 2 len.n/br 1=2 c C 1 do if .X C j /n 6 Xn C j .mod Xr 1/ in the ring Zn ŒX then return false 6. return true
Fig. 21.1. Algorithm AKS 21.2.1 Running time analysis The question of the running time of Algorithm AKS is settled by the following fact: Theorem 21.2. For integers n > 1 and m 1, the least prime r such that r − n and the multiplicative order of Œnr 2 Zr is greater than m is O.m2 len.n//. Proof. Call a prime r “good” if r − n and the multiplicative order of Œnr 2 Zr is greater than m, and otherwise call r “bad.” If r is bad, then either r j n or r j .nd 1/ for some d D 1; : : : ; m. Thus, any bad prime r satisfies r jn
m Y
.nd
1/:
d D1
If all primes r up to some given bound x 2 are bad, then the product of all primes Q d up to x divides n m 1/, and so in particular, d D1 .n Y rx
r n
m Y
.nd
d D1
1/;
550
Deterministic primality testing
where the first product is over all primes r up to x. Taking logarithms, we obtain Y m m X X d log r log n .n 1/ .log n/ 1 C d rx
d D1
d D1
D .log n/.1 C m.m C 1/=2/: But by Theorem 5.7, we have X
log r cx
rx
for some constant c > 0, from which it follows that xc
1
.log n/.1 C m.m C 1/=2/;
and the theorem follows. From this theorem, it follows that the value of r found in step 2 — which need not be prime—will be O.len.n/5 /. From this, we obtain: Theorem 21.3. Algorithm AKS can be implemented so as to run in time O.len.n/16:5 /. Proof. As discussed above, the value of r determined in step 2 will be O.len.n/5 /. It is fairly straightforward to see that the running time of the algorithm is dominated by the running time of step 5. Here, we have to perform O.r 1=2 len.n// exponentiations to the power n in the ring Zn ŒX=.Xr 1/. Each of these exponentiations takes O.len.n// operations in Zn ŒX=.Xr 1/, each of which takes O.r 2 / operations in Zn , each of which takes time O.len.n/2 /. This yields a running time bounded by a constant times r 1=2 len.n/ len.n/ r 2 len.n/2 D r 2:5 len.n/4 : Substituting the bound O.len.n/5 / for r, we obtain the stated bound in the theorem.
21.2.2 Correctness As for the correctness of Algorithm AKS, we first show: Theorem 21.4. If the input to Algorithm AKS is prime, then the output is true. Proof. Assume that the input n is prime. The test in step 1 will certainly fail. If the algorithm does not return true in step 3, then certainly the test in step 4 will fail as well. If the algorithm reaches step 5, then all of the tests in the loop in step 5 will fail —this follows from Theorem 21.1.
21.2 The algorithm and its analysis
551
The interesting case is the following: Theorem 21.5. If the input to Algorithm AKS is composite, then the output is false. The proof of this theorem is rather long, and is the subject of the remainder of this section. Suppose the input n is composite. If n is a prime power, then this will be detected in step 1, so we may assume that n is not a prime power. Assume that the algorithm has found a suitable value of r in step 2. Clearly, the test in 3 will fail. If the test in step 4 passes, we are done, so we may assume that this test fails; that is, we may assume that all prime factors of n are greater than r. Our goal now is to show that one of the tests in the loop in step 5 must pass. The proof will be by contradiction: we shall assume that none of the tests pass, and derive a contradiction. The assumption that none of the tests in step 5 fail means that in the ring Zn ŒX, the following congruences hold: .X C j /n Xn C j .mod Xr
1/ .j D 1; : : : ; 2 len.n/br 1=2 c C 1/:
(21.2)
For the rest of the proof, we fix a particular prime divisor p of n — the choice of p does not matter. Since p j n, we have a natural ring homomorphism from Zn ŒX to Zp ŒX (see Examples 7.52 and 7.46), which implies that the congruences (21.2) hold in the ring of polynomials over Zp as well. From now on, we shall work exclusively with polynomials over Zp . Let us state in somewhat more abstract terms the precise assumptions we are making in order to derive our contradiction: (A0) n > 1, r > 1, and ` 1 are integers, p is a prime dividing n, and gcd.n; r/ D 1; (A1) n is not a prime power; (A2) p > r; (A3) the congruences .X C j /n Xn C j .mod Xr
1/ .j D 1; : : : ; `/
hold in the ring Zp ŒX; (A4) the multiplicative order of Œnr 2 Zr is greater than 4 len.n/2 ; (A5) ` > 2 len.n/br 1=2 c. The rest of the proof will rely only on these assumptions, and not on any other details of Algorithm AKS. From now on, only assumption (A0) will be implicitly in force. The other assumptions will be explicitly invoked as necessary. Our goal
552
Deterministic primality testing
is to show that assumptions (A1), (A2), (A3), (A4), and (A5) cannot all be true simultaneously. Define the Zp -algebra E WD Zp ŒX=.Xr 1/, and let WD ŒXXr 1 2 E, so that E D Zp Œ. Every element of E can be expressed uniquely as g./ D ŒgXr 1 , for g 2 Zp ŒX of degree less than r, and for an arbitrary polynomial g 2 Zp ŒX, we have g./ D 0 if and only if .Xr 1/ j g. Note that 2 E and has multiplicative order r: indeed, r D 1, and s 1 cannot be zero for s < r, since Xs 1 has degree less than r. Assumption (A3) implies that we have a number of interesting identities in the Zp -algebra E: . C j /n D n C j .j D 1; : : : ; `/: For the polynomials gj WD X C j 2 Zp ŒX, with j in the given range, these identities say that gj ./n D gj . n /. In order to exploit these identities, we study more generally functions k , for various integer values k, that send g./ 2 E to g. k /, for arbitrary g 2 Zp ŒX, and we investigate the implications of the assumption that such functions behave like the kth power map on certain inputs. To this end, let Z.r/ denote the set of all positive integers k such that gcd.r; k/ D 1. Note that the set Z.r/ is multiplicative; that is, 1 2 Z.r/ , and for all k; k 0 2 Z.r/ , we have kk 0 2 Z.r/ . Also note that because of our assumption (A0), both n and p are in Z.r/ . For integer k 2 Z.r/ , let O k W Zp ŒX ! E be the polynomial evaluation map that sends g 2 Zp ŒX to g. k /. This is of course a Zp -algebra homomorphism, and we have: Lemma 21.6. For all k 2 Z.r/ , the kernel of O k is .Xr is E.
1/, and the image of O k
Proof. Let J WD Ker O k , which is an ideal of Zp ŒX. Let k 0 be a positive integer such that kk 0 1 .mod r/, which exists because gcd.r; k/ D 1. To show that J D .Xr 1/, we first observe that O k .Xr
1/ D . k /r
1 D . r /k
1 D 1k
1 D 0;
and hence .Xr 1/ J . Next, we show that J .Xr 1/. Let g 2 J . We want to show that .Xr 1/ j g. Now, g 2 J means that g. k / D 0. If we set h WD g.Xk /, this implies that h./ D 0, which means that .Xr 1/ j h. So let us write h D .Xr 1/f , for some f 2 Zp ŒX. Then 0
0
0
g./ D g. kk / D h. k / D . k r which implies that .Xr
1/ j g.
0
1/f . k / D 0;
21.2 The algorithm and its analysis
553
That finishes the proof that J D .Xr 1/. Finally, to show that O k is surjective, suppose we are given an arbitrary element 0 of E, which we can express as g./ for some g 2 Zp ŒX. Now set h WD g.Xk /, and observe that 0
O k .h/ D h. k / D g. kk / D g./: Because of Lemma 21.6, then by Theorem 7.26, the map k W E ! E that sends g./ 2 E to g. k /, for g 2 Zp ŒX, is well defined, and is a ring automorphism — indeed, a Zp -algebra automorphism — on E. Note that for all k; k 0 2 Z.r/ , we have 0
k D k 0 if and only if k D k if and only if k k 0 .mod r/, and k B k 0 D k 0 B k D kk 0 . So in fact, the set of all k forms an abelian group (with respect to composition) that is isomorphic to Zr . Remark. It is perhaps helpful (but not necessary for the proof) to examine the behavior of the map k in a bit more detail. Let ˛ 2 E, and let ˛D
r 1 X
ai i
iD0
be the canonical representation of ˛. Since gcd.r; k/ D 1, the map W f0; : : : ; r 1g ! f0; : : : ; r 1g that sends i to ki mod r is a permutation whose inverse is the permutation 0 that sends i to k 0 i mod r, where k 0 is a multiplicative inverse of k modulo r. Then we have k .˛/ D
r 1 X iD0
ai ki D
r 1 X i D0
ai .i / D
r 1 X
a 0 .i / i :
iD0
Thus, the action of k is to permute the coordinate vector .a0 ; : : : ; ar 1 / of ˛, sending ˛ to the element in E whose coordinate vector is .a 0 .0/ ; : : : ; a 0 .r 1/ /. So we see that although we defined the maps k in a rather “highbrow” algebraic fashion, their behavior in concrete terms is actually quite simple.
Recall that the pth power map on E is a Zp -algebra homomorphism (see Theorem 19.7), and so for all ˛ 2 E, if ˛ D g./ for g 2 Zp ŒX, then (by Theorem 16.6) we have ˛ p D g./p D g. p / D p .˛/: Thus, p acts just like the pth power map on all elements of E. We can restate assumption (A3) as follows: n . C j / D . C j /n .j D 1; : : : ; `/:
554
Deterministic primality testing
That is to say, the map n acts just like the nth power map on the elements C j for j D 1; : : : ; `. Now, although the p map must act like the pth power map on all of E, there is no good reason why the n map should act like the nth power map on any particular element of E, and so the fact that it does so on all the elements C j for j D 1; : : : ; ` looks decidedly suspicious. To turn our suspicions into a contradiction, let us start by defining some notation. For ˛ 2 E, let us define C.˛/ WD fk 2 Z.r/ W k .˛/ D ˛ k g; and for k 2 Z.r/ , let us define D.k/ WD f˛ 2 E W k .˛/ D ˛ k g: In words: C.˛/ is the set of all k for which k acts like the kth power map on ˛, and D.k/ is the set of all ˛ for which k acts like the kth power map on ˛. From the discussion above, we have p 2 C.˛/ for all ˛ 2 E, and it is also clear that 1 2 C.˛/ for all ˛ 2 E. Also, it is clear that ˛ 2 D.p/ for all ˛ 2 E, and 1E 2 D.k/ for all k 2 Z.r/ . The following two simple lemmas say that the sets C.˛/ and D.k/ are multiplicative. Lemma 21.7. For every ˛ 2 E, if k 2 C.˛/ and k 0 2 C.˛/, then kk 0 2 C.˛/. 0
Proof. If k .˛/ D ˛ k and k 0 .˛/ D ˛ k , then 0
0
0
0
kk 0 .˛/ D k .k 0 .˛// D k .˛ k / D .k .˛//k D .˛ k /k D ˛ kk ; where we have made use of the homomorphic property of k . Lemma 21.8. For every k 2 Z.r/ , if ˛ 2 D.k/ and ˇ 2 D.k/, then ˛ˇ 2 D.k/. Proof. If k .˛/ D ˛ k and k .ˇ/ D ˇ k , then k .˛ˇ/ D k .˛/k .ˇ/ D ˛ k ˇ k D .˛ˇ/k ; where again, we have made use of the homomorphic property of k . Let us define s to be the multiplicative order of Œpr 2 Zr , and t to be the order of the subgroup of Zr generated by Œpr and Œnr . Since r j .p s 1/, if we take any extension field F of degree s over Zp (which we know exists by Theorem 19.12), then since F is cyclic (Theorem 7.29) and has order p s 1, we know that there exists an element 2 F of multiplicative order r (Theorem 6.32). Let us define the polynomial evaluation map O W Zp ŒX ! F
21.2 The algorithm and its analysis
555
that sends g 2 Zp ŒX to g./ 2 F . Since Xr 1 is clearly in the kernel of O , then by Theorem 7.27, the map W E ! F that sends g./ to g./, for g 2 Zp ŒX, is a well-defined ring homomorphism, and actually, it is a Zp -algebra homomorphism. For concreteness, one could think of F as Zp ŒX=.f /, where f is an irreducible factor of Xr 1 of degree s. In this case, we could simply take to be ŒXf (see Example 19.1), and the map O above would be just the natural map from Zp ŒX to Zp ŒX=.f /. The key to deriving our contradiction is to examine the set S WD .D.n//, that is, the image under of the set D.n/ of all elements ˛ 2 E for which n acts like the nth power map. Lemma 21.9. Under assumption (A1), we have jS j n2bt
1=2 c
:
Proof. Consider the set of integers I WD fnu p v W u; v D 0; : : : ; bt 1=2 cg: We first claim that jI j > t . To prove this, we first show that each distinct pair .u; v/ gives rise to a distinct value nu p v . To this end, we make use of our assumption (A1) that n is not a prime power, and so is divisible by some prime q other than p. Thus, if .u0 ; v 0 / ¤ .u; v/, then either u ¤ u0 , in which case the power of q in the prime factorization of nu p v is 0 0 different from that in nu p v , or u D u0 and v ¤ v 0 , in which case the power of p in the prime factorization 0 0 of nu p v is different from that in nu p v . The claim now follows from the fact that both u and v range over a set of size bt 1=2 c C 1 > t 1=2 , and so there are strictly more than t such pairs .u; v/. Next, recall that t was defined to be the order of the subgroup of Zr generated by Œnr and Œpr ; equivalently, t is the number of distinct residue classes of the form Œnu p v r , where u and v range over all non-negative integers. Since each element of I is of the form nu p v , and jI j > t , we may conclude that there must be two distinct elements of I , call them k and k 0 , that are congruent modulo r. Furthermore, any element of I is a product of two positive integers each of which 1=2 1=2 is at most nbt c , and so both k and k 0 lie in the range 1; : : : ; n2bt c . Now, let ˛ 2 D.n/. This is equivalent to saying n 2 C.˛/. We always have 1 2 C.˛/ and p 2 C.˛/, and so by Lemma 21.7, we have nu p v 2 C.˛/ for all non-negative integers u; v, and so in particular, k; k 0 2 C.˛/. Since both k and k 0 are in C.˛/, we have 0
k .˛/ D ˛ k and k 0 .˛/ D ˛ k :
556
Deterministic primality testing
Since k k 0 .mod r/, we have k D k 0 , and hence 0
˛k D ˛k : Now apply the homomorphism , obtaining 0
.˛/k D .˛/k : Since this holds for all ˛ 2 D.n/, we conclude that all elements of S are roots 0 0 of the polynomial Xk Xk : Since k ¤ k 0 , we see that Xk Xk is a non-zero 1=2 polynomial of degree at most maxfk; k 0 g n2bt c , and hence can have at most 1=2 n2bt c roots in the field F (Theorem 7.14). Lemma 21.10. Under assumptions (A2) and (A3), we have jS j 2min.t;`/
1:
Proof. Let m WD min.t; `/. Under assumption (A3), we have C j 2 D.n/ for j D 1; : : : ; m. Under assumption (A2), we have p > r > t m, and hence the integers j D 1; : : : ; m are distinct modulo p. Define Y m m X ej P WD .X C j / 2 Zp ŒX W ej 2 f0; 1g for j D 1; : : : ; m; and ej < m : j D1
j D1
That is, we form P by taking products over all subsets S ¨ fXCj W j D 1; : : : ; mg. Clearly, jP j D 2m 1. Define P ./ WD ff ./ 2 E W f 2 P g and P ./ WD ff ./ 2 F W f 2 P g. Note that .P .// D P ./, and that by Lemma 21.8, P ./ D.n/. Therefore, to prove the lemma, it suffices to show that jP ./j D 2m 1. Suppose that this is not the case. This would give rise to distinct polynomials g; h 2 Zp ŒX, both of degree at most t 1, such that g./ 2 D.n/; h./ 2 D.n/; and .g.// D .h.//: So we have n 2 C.g.// and (as always) 1; p 2 C.g.//. Likewise, we have 1; n; p 2 C.h.//. By Lemma 21.7, for all integers k of the form nu p v , where u and v range over all non-negative integers, we have k 2 C.g.// and k 2 C.h.//: For each such k, since .g.// D .h.//, we have .g.//k D .h.//k , and
21.2 The algorithm and its analysis
557
hence 0 D .g.//k
.h.//k
D .g./k /
.h./k / ( is a homomorphism)
D .g. k //
.h. k // (k 2 C.g.// and k 2 C.h.//)
D g. k /
h. k / (definition of ):
Thus, the polynomial f WD g h 2 Zp ŒX is a non-zero polynomial of degree at most t 1, having roots k in the field F for all k of the form nu p v . Now, t is by definition the number of distinct residue classes of the form Œnu p v r 2 Zr . Also, 0 since has multiplicative order r, for integers k; k 0 , we have k D k if and only if k k 0 .mod r/. Therefore, as k ranges over all integers of the form nu p v , k ranges over precisely t distinct values in F . But since all of these values are roots of the polynomial f , which is non-zero and of degree at most t 1, this is impossible (Theorem 7.14). We are now (finally!) in a position to complete the proof of Theorem 21.5. Under assumptions (A1), (A2), and (A3), Lemmas 21.9 and 21.10 imply that 2min.t;`/
1 jSj n2bt
1=2 c
:
(21.3)
The contradiction is provided by the following: Lemma 21.11. Under assumptions (A4) and (A5), we have 2min.t;`/
1 > n2bt
1=2 c
:
Proof. Observe that log2 n len.n/, and so it suffices to show that 2min.t;`/
1 > 22 len.n/bt
1=2 c
;
and for this, it suffices to show that min.t; `/ > 2 len.n/bt 1=2 c; since for all integers a; b with a > b 1, we have 2a > 2b C 1. To show that t > 2 len.n/bt 1=2 c, it suffices to show that t > 2 len.n/t 1=2 , or equivalently, that t > 4 len.n/2 . But observe that by definition, t is the order of the subgroup of Zr generated by Œnr and Œpr , which is at least as large as the multiplicative order of Œnr in Zr , and by assumption (A4), this is larger than 4 len.n/2 . Finally, directly by assumption (A5), we have ` > 2 len.n/bt 1=2 c. That concludes the proof of Theorem 21.5.
558
Deterministic primality testing
E XERCISE 21.1. Show that if Conjecture 5.24 is true, then the value of r discovered in step 2 of Algorithm AKS satisfies r D O.len.n/2 /. 21.3 Notes The algorithm presented here is due to Agrawal, Kayal, and Saxena [6]. If fast algorithms for integer and polynomial arithmetic are used, then using the analysis presented here, it is easy to see that the algorithm runs in time O.len.n/10:5Co.1/ /. More generally, it is easy to see that the algorithm runs in time O.r 1:5Co.1/ len.n/3Co.1/ /, where r is the value determined in step 2 of the algorithm. In our analysis of the algorithm, we were able to obtain the bound r D O.len.n/5 /, leading to the running-time bound O.len.n/10:5Co.1/ /. Using Fouvry’s result, one can show that r D O.len.n/3 /, leading to a running-time bound of O.len.n/7:5Co.1/ /. Moreover, if Conjecture 5.24 on the density of Sophie Germain primes is true, then one could show that r D O.len.n/2 / (see Exercise 21.1), which would lead to a running-time bound of O.len.n/6Co.1/ /. Prior to this algorithm, the fastest deterministic, rigorously proved primality test was one introduced by Adleman, Pomerance, and Rumely [5], called the Jacobi sum test, which runs in time O.len.n/c len.len.len.n/// / for some constant c. Note that for numbers n with less than 2256 bits, the value of len.len.len.n/// is at most 8, and so this algorithm runs in time O.len.n/8c / for any n that one could ever actually write down. We also mention the earlier work of Adleman and Huang [3], who gave a probabilistic algorithm whose output is always correct, and which runs in expected polynomial time (i.e., a Las Vegas algorithm, in the parlance of §9.7).
Appendix: Some useful facts
A1. Some handy inequalities. The following inequalities involving exponentials and logarithms are very handy. (i) For all real x, we have 1 C x ex ; or, taking logarithms, log.1 C x/ x: (ii) For all real x 0, we have e
x
1
x C x 2 =2;
or, taking logarithms, x C x 2 =2/:
x log.1
(iii) For all real x with 0 x 1=2, we have 1
xe
x x2
e
2x
;
or, taking logarithms, log.1
x/
x
x2
2x:
(i) and (ii) follow easily from Taylor’s formula with remainder, applied to the function e x , while (iii) may be proved by expanding log.1 x/ as a Taylor series, and making a simple calculation. A2. Binomial coefficients. For integers n and k, with 0 k n, one defines the binomial coefficient ! n nŠ WD : k kŠ.n k/Š 559
560
Appendix: Some useful facts
We have the identities ! ! n n D D 1; n 0 and for 0 < k < n, we have Pascal’s identity ! ! ! n n 1 n 1 D C ; k k 1 k which may be verified by direct calculation. From these identities, it follows that kn is an integer, and indeed, is equal to the number of subsets of f1; : : : ; ng of cardinality k. The usual binomial theorem also follows as an immediate consequence: for numbers a; b, and positive integer n, we have the binomial expansion ! n X n .a C b/n D an k b k : k kD0
It is also easily verified that if we fix n, then the function f .k/ WD kn is symmetric about the line k D n=2, is increasing on the interval Œ0; n=2, and is decreasing on the interval Œn=2; n. That is, f .k/ D f .n k/ for 0 k n, f .k/ < f .k C 1/ for 0 k < n=2, and f .k/ > f .k C 1/ for n=2 k < n. A3. Countably infinite sets. Let ZC WD f1; 2; 3; : : :g, the set of positive integers. A set S is called countably infinite if there is a bijection f W ZC ! S ; in this case, we can enumerate the elements of S as x1 ; x2 ; x3 ; : : : ; where xi WD f .i/. A set S is called countable if it is either finite or countably infinite. For a set S, the following conditions are equivalent: S is countable; there is a surjective function g W ZC ! S ; there is an injective function h W S ! ZC . The following facts can be easily established: (i) if S1 ; : : : ; Sn are countable sets, then so are S1 [ [ Sn and S1 Sn ; S (ii) if S1 ; S2 ; S3 ; : : : are countable sets, then so is 1 i D1 Si ; S1 (iii) if S is a countable set, then so is the set i D0 S i of all finite sequences of elements in S .
Appendix: Some useful facts
561
Some examples of countably infinite sets: Z, Q, the set of all finite bit strings. Some examples of uncountable sets: R, the set of all infinite bit strings. A4. Integrating piece-wise continuous functions. In discussing the Riemann Rb integral a f .t /dt, many introductory calculus texts only discuss in any detail the case where the integrand f is continuous on the closed interval Œa; b, in which case the integral is always well defined. However, the Riemann integral is well defined for much broader classes of functions. For our purposes in this text, it is convenient and sufficient to work with integrands that are piece-wise continuous on Œa; b, that is, there exist real numbers x0 ; x1 ; : : : ; xk and functions f1 ; : : : ; fk , such that a D x0 x1 xk D b, and for i D 1; : : : ; k, the function fi is continuous on the closed interval Œxi 1 ; xi , and agrees with f on the open interval .xi 1 ; xi /. In this case, f is integrable on Œa; b, and indeed Z b k Z xi X f .t /dt D fi .t /dt: a
i D1 xi
1
It is not hard to prove this equality, using the basic definition of the Riemann integral; however, for our purposes, we can also just take the value of the expression on the right-hand side as the definition of the integral on the left-hand side. If f is piece-wise continuous on Œa; b, then it is also bounded on Œa; b, that is, there exists a positive number M such that jf .t /j M for all t 2 Œa; b, Rb from which it follows that j a f .t /dt j M.b a/. We also say that f is piece-wise continuous on Œa; 1/ if for all b a, f is piece-wise continuous on Œa; b. In this case, we may define the improper R1 Rb integral a f .t /dt as the limit, as b ! 1, of a f .t /dt, provided the limit exists. A5. Estimating sums by integrals. Using elementary calculus, it is easy to estimate sums over a monotone sequences in terms of a definite integral, by interpreting the integral as the area under a curve. Let f be a real-valued function that is (at least piece-wise) continuous and monotone on the closed interval Œa; b, where a and b are integers. Then we have Z b b X min.f .a/; f .b// f .i / f .t /dt max.f .a/; f .b//: i Da
a
P A6. Infinite series. Consider an infinite series 1 i D1 xi . It is a basic fact from P calculus that if the xi ’s are non-negative and 1 i D1 xi converges to a value
562
Appendix: Some useful facts
y, then any infinite series whose terms are a rearrangement of the xi ’s converges to the same value y. If we drop the requirement that the xi ’s are non-negative, but insist that the P P1 series 1 called absolutely i D1 jxi j converges, then the series i D1 xi isP convergent. In this case, then not only does the series 1 i D1 xi converge to some value y, but any infinite series whose terms are a rearrangement of the xi ’s also converges to the same value y. A7. Double infinite series. The topic of double infinite series may not be discussed in a typical introductory calculus course; we summarize here the basic facts that we need. Suppose that fxij g1 i;j D1 is a family non-negative real numbers such that for P each i , the series j xij converges to a value ri , and for each j the series P x converges to a value cj . Then we can form the double infinite series Pi Pij P P P P i j xij D i ri and the double infinite series j i xij D j cj . If .i1 ; j1 /; .i2 ; j2 /; : : : is an enumeration of all pairs of indices .i; j /, we can P P P also form the single infinite series k xik jk . We then have i j xij D P P P j i xij D k xik jk , where the three series either all converge to the same value, or all diverge. Thus, we can reverse the order of summation in a double infinite series of non-negative terms. If we drop the non-negativity P requirement, the same result holds provided k jxik jk j < 1. P Now suppose i ai is an infinite series of non-negative terms that conP verges to A, and that j bj is an infinite series of non-negative terms that converges to B. If .i1 ; j1 /; .i2 ; j2 /; : : : is an enumeration of all pairs of inP dices .i; j /, then k aik bjk converges to AB. Thus, we can multiply termwise infinite series with non-negative terms. If we drop the non-negativity P P requirement, the same result holds provided i ai and j bj converge absolutely. A8. Convex functions. Let I be an interval of the real line (open, closed, or half open, bounded or unbounded), and let f be a real-valued function defined on I . The function f is called convex on I if for all x0 ; x2 2 I , and for all t 2 Œ0; 1, we have f .tx0 C .1 t /x2 / tf .x0 / C .1 t/f .x2 /. Geometrically, convexity means that for every three points Pi D .xi ; f .xi //, i D 0; 1; 2, where each xi 2 I and x0 < x1 < x2 , the point P1 lines on or below the line through P0 and P2 . We state here the basic analytical facts concerning convex functions: (i) if f is convex on I , then f is continuous on the interior of I (but not necessarily at the endpoints of I , if any); (ii) if f is continuous on I and differentiable on the interior of I , then
Appendix: Some useful facts
563
f is convex on I if and only if its derivative is non-decreasing on the interior of I .
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Index of notation
Entries are listed in order of appearance.
log: natural logarithm, xiv exp: exponential function, xiv Z: the integers, xiv Q: the rationals, xiv R: the reals, xiv C: the complex numbers, xiv 1: arithmetic with infinity, xiv Œa; b; .a; b/, etc.: interval notation, xv ;; 2; ; ¨; [; \; n; j j: set notation, xv S1 Sn ; S n : Cartesian product, xv fxi gi2I : family, xv 1 fxi gn iDm ; fxi giDm : sequence, xv f .S /: image of a set, xvi f 1 : pre-image of a set/inverse function, xvi f B g: function composition, xvi a j b: a divides b, 1 bxc: floor of x, 4 dxe: ceiling of x, 4 a mod b: integer remainder, 4 aZ: ideal generated by a, 5 I1 C I2 : sum of ideals, 5 gcd: greatest common divisor, 7 p .n/: largest power to which p divides n, 10 lcm: least common multiple, 11 a b .mod n/: a congruent to b modulo n, 15 b=a mod n: integer remainder, 21 a 1 mod n: integer modular inverse, 21 Œan , Œa: residue class of a modulo n, 23 Zn : residue classes modulo n, 24 Z n : invertible residue classes, 27 : Euler’s phi function, 31 m .Z n / : mth powers in Zn , 35 : Möbius function, 45 O; ; ‚; o; : asymptotic notation, 49 len: length (in bits) of an integer, 61 rep.˛/: canonical representative of ˛ 2 Zn , 64 .x/: number of primes up to x, 103 #: Chebyshev’s theta function, 106
li: logarithmic integral, 116 : Riemann’s zeta function, 117 Map.I; G/: group of functions f W I ! G, 130 mG: the subgroup fma W a 2 Gg, 131 Gfmg: the subgroup fa 2 G W ma D 0G g, 132 G m : multiplicative subgroup fam W a 2 Gg, 132 H1 C H2 : sum of subgroups, 134 H1 H2 : product of subgroups, 135 a b .mod H /: a b 2 H , 136 ŒaH : coset of H containing a, 137 G=H : quotient group, 139 ŒG W H : index, 139 Ker : kernel, 142 Im : image, 142 G Š G 0 : isomorphic groups, 145 Hom.G; G 0 /: group homomorphisms G ! G 0 , 150 hai: subgroup generated by a, 152 ha1 ; : : : ; ak i: subgroup generated by a1 ; : : : ; ak , 152 ˛: N complex conjugate of ˛, 166 N.˛/: norm of ˛ 2 C, 166 Map.I; R/: ring of functions f W I ! R, 167 AB: ring-theoretic product, 168 a j b: a divides b, 169 R : multiplicative group of units of R, 169 ZŒi : Gaussian integers, 173 Q.m/ : fa=b W gcd.b; m/ D 1g, 173 RŒX: ring of polynomials, 175 deg.g/: degree of a polynomial, 176 lc.g/: leading coefficient of a polynomial, 176 g mod h: polynomial remainder, 177 aR: ideal generated by a, 185 .a1 ; : : : ; ak /: ideal generated by a1 ; : : : ; ak , 185 R=I : quotient ring, 186 a b .mod d /: a b 2 dR, 186 Œad : the residue class ŒadR , 186 RŒ˛: smallest subring containing R and ˛, 191
570
Index of notation RŒ˛1 ; : : : ; ˛n : smallest subring containing R and ˛1 ; : : : ; ˛n , 192 R Š R0 : isomorphic rings, 194 P: probability distribution, 206 P1 P2 ; Pn 1 : product distribution, 210 PŒA j B: conditional probability of A given B, 213 EŒX : expected value of X , 233 VarŒX : variance of X , 235 EŒX j B: conditional expectation of X given B, 237 ŒX I Y : statistical distance, 259 log ˛: discrete logarithm, 325 .a j p/: Legendre symbol, 340 .a j n/: Jacobi symbol, 344 Jn : Jacobi map, 345 Map.I; M /: R-module of functions f W I ! M , 359 cM : submodule fc˛ W ˛ 2 M g, 360 M fcg: submodule f˛ 2 M W c˛ D 0M g, 360 R˛: submodule fc˛ W c 2 Rg, 360 h˛1 ; : : : ; ˛k iR : submodule generated by ˛1 ; : : : ; ˛k , 360 RŒX<` : polynomials of degree less than `, 360 M=N : quotient module, 361 M Š M 0 : isomorphic modules, 364 HomR .M; M 0 /: R-linear maps M ! M 0 , 365 dimF .V /: dimension, 371 A.i; j /: .i; j / entry of A, 377 Rowi .A/: i th row of A, 377 Colj .A/: j th column of A, 377 Rmn : m n matrices over R, 377 mn 0R : m n zero matrix, 377 A > : transpose of A, 379 VecS .˛/: coordinate vector, 381 MatS;T ./: matrix of linear map, 382 ‰.y; x/: number of y-smooth integers up to x, 398 Map.I; E /: R-algebra of functions f W I ! E , 421 RŒ˛: subalgebra generated by ˛, 424 gcd: greatest common divisor (polynomial), 430 lcm: least common multiple (polynomial), 432 h=g mod f : polynomial remainder, 434 g 1 mod f : polynomial modular inverse, 434 .E W F /: degree of an extension, 439 F .˛/: smallest subfield containing F and ˛, 439 RŒŒX: formal power series, 444 R..X//: formal Laurent series, 445 R..X 1 //: reversed Laurent series, 447 deg.g/: degree of g 2 R..X 1 //, 447 lc.g/: leading coefficient of a 2 R..X 1 //, 447 bgc: floor of g 2 R..X 1 //, 447 len: length of a polynomial, 464 rep.˛/: canonical representative of ˛ 2 RŒX=.f /, 464 DF .V /: dual space, 490 LF .V /: space of linear transformations, 499 NE=F .˛/: norm, 517 TrE=F .˛/: trace, 517
571
Index
Abel’s identity, 110 abelian group, 125 additive identity, 26 additive inverse, 26 additive subgroup, 168 Adleman, L. M., 98, 102, 419, 545, 558 Agrawal, M., 547, 558 Alford, W., 323 algebra, 420 algebraic element, 439 extension, 439 almost universal hash functions, 256 Apostol, T. M., 124 approximately computes, 300 arithmetic function, 45 arithmetic/geometric mean, 239 Artin’s conjecture, 97 associate elements of an integral domain, 449 polynomials, 428 associative binary operation, xvii asymptotic notation, 49 Atlantic City algorithm, 301 automorphism algebra, 422 group, 145 module, 364 ring, 194 vector space, 369 baby step/giant step method, 328 Bach, E., 124, 303, 323, 338, 355, 545 basis, 366 Bateman, P., 124 Bayes’ theorem, 215 Bellare, M., 419 Ben-Or, M., 545 Berlekamp subalgebra, 537 Berlekamp’s algorithm, 537 Berlekamp, E. R., 506, 545 Bernoulli trial, 207 Bertrand’s postulate, 107
big-O, -Omega, -Theta, 49 bijection, xvi bijective, xvi binary gcd algorithm, 76 binary operation, xvii binomial coefficient, 559 binomial distribution, 222, 238 binomial expansion, 560 binomial theorem, 168, 560 birthday paradox, 247 bivariate polynomial, 182 Blum, L., 102 Blum, M., 102 Boneh, D., 102, 339 Bonferroni’s inequalities, 212 Boole’s equality, 209 Boole’s inequality, 209 Boolean circuits, 71 Brent, R. P., 482 Brillhart, J., 418 Buhler, J. P., 418 Burgess, D. A., 356 C, xiv cancellation law, 2, 20, 27, 128, 170, 433 Canfield, E., 418 canonical representative integer, 64 polynomial, 464 Cantor, D. G., 545 Cantor–Zassenhaus algorithm, 529 cardinality, xv Carmichael number, 306 Carmichael, R. D., 323 Carter, J. L., 274 Cartesian product, xv ceiling, 4 characteristic of a ring, 168 characteristic polynomial, 517 Chebyshev’s inequality, 240 Chebyshev’s theorem, 103 Chebyshev’s theta function, 106 Chernoff bound, 241
572
Index Chinese remainder theorem general, 201 integer, 21, 81 polynomial, 434, 471 Chistov, A. L., 545 classification of cyclic groups, 155 closed under, xvii column null space, 394 column rank, 394 column space, 394 column vector, 377 common divisor in an integral domain, 450 integer, 6 polynomial, 429 common multiple in an integral domain, 450 integer, 10 polynomial, 431 commutative binary operation, xvii commutative ring with unity, 165 companion matrix, 384 complex conjugation, 166, 197 composite, 2 composition, xvi conditional distribution, 212, 223 conditional expectation, 237 conditional probability, 213 congruence, 15, 136 conjugacy class, 515 conjugate, 515 constant polynomial, 175 constant term, 176 continued fraction method, 418 continued fractions, 101 convex function, 562 coordinate vector, 381 of a projection, 490 Coppersmith, D., 419 Cormen, T. H., 338 coset, 137 countable, 560 countably infinite, 560 covariance, 239 Crandall, R., 72, 124, 419 cyclic, 152 Damgård, I., 323, 462 Davenport, J., 101 decisional Diffie–Hellman problem, 336 degree of a polynomial, 176 of a reversed Laurent series, 447 of an element in an extension field, 439 of an extension, 439 DeMorgan’s law, 207 Denny, T., 419 derivative, 442 deterministic algorithm, 276 deterministic poly-time equivalent, 334 deterministic poly-time reducible, 334
Dickson, L. E., 124 Diffie, W., 339 Diffie–Hellman key establishment protocol, 333 Diffie–Hellman problem, 333 dimension, 371 direct product of algebras, 421 of groups, 129 of modules, 359 of rings, 167 Dirichlet inverse, 48 Dirichlet product, 45 Dirichlet series, 119 Dirichlet’s theorem, 120 Dirichlet, G., 124 discrete logarithm, 325 algorithm for computing, 327, 399 discrete probability distribution, 269 discriminant, 181 disjoint, xvi distinct degree factorization, 529, 542 distributive law Boolean, 207 divides, 1, 169 divisible by, 1, 169 division with remainder property integer, 3 polynomial, 177 divisor, 1, 169 Dixon, J., 418 Dornstetter, J. L., 506 dual space, 490 Durfee, G., 102 Eisenstein integers, 454 Eisenstein’s criterion, 460 elementary row operation, 388 elliptic curve method, 418 equal degree factorization, 531, 536 equivalence class, 14 equivalence relation, 14 Eratosthenes sieve of, 114 Erd˝os, P., 418 error correcting code, 95, 475 error probability, 300 essentially equal, 212 Euclidean algorithm extended integer, 76 polynomial, 468 integer, 73 polynomial, 467 Euclidean domain, 451 Euler’s criterion, 37, 164, 204 Euler’s identity, 117 Euler’s phi function, 31 and factoring, 318 Euler’s summation formula, 113 Euler’s theorem, 33, 156 Euler’s totient function, 31
573
574
Index
Euler, L., 123 event, 207 eventually positive, 49 exp, xiv expectation, 233 expected polynomial time, 281 expected running time, 281 expected value, 233 exponent, 159 module, 362 extended Euclidean algorithm integer, 76 polynomial, 468 extended Gaussian elimination, 390 extension, xvi extension field, 174, 438 extension ring, 173
integer, 7 polynomial, 430 generating polynomial, 485 generator, 152 algorithm for finding, 325 geometric distribution, 270, 273 Gerhard, J., 483, 545 Goldwasser, S., 356 Gordon, D. M., 419 Gordon, J., 545 Granville, A., 323 greatest common divisor in an integral domain, 450 integer, 6 polynomial, 429 group, 125 Guy, M., 101
factoring and Euler’s phi function, 318 factoring algorithm integer, 406, 413 deterministic, 482 polynomial, 529, 537 deterministic, 543 family, xv fast Fourier transform, 479 Fermat’s little theorem, 33, 35 FFT, 479 field, 169 field of fractions, 425 finite dimensional, 371 finite expectation, 271 finite extension, 439 finite fields existence, 509 subfield structure, 513 uniqueness, 513 finitely generated abelian group, 152 module, 360 fixed field, 513 floor, 4 floor function reversed Laurent series, 447 formal derivative, 442 formal Laurent series, 445 formal power series, 444 Frandsen, G., 462 Frobenius map, 510 fundamental theorem of arithmetic, 2 fundamental theorem of finite abelian groups, 162 fundamental theorem of finite dimensional F ŒX-modules, 504 Fürer, M., 71
Hadamard, J., 123 Halberstam, H., 323 Hardy, G. H., 102, 123, 124 hash function, 251 Heath-Brown, D., 124 Hellman, M., 338, 339 Hensel lifting, 349 homomorphism algebra, 422 group, 141 module, 362 ring, 190 vector space, 369 Horn, R., 124 Horner’s rule, 465 Huang, M.-D., 558 hybrid argument, 262
von zur Gathen, J., 483, 545, 546 Gauss’ lemma, 342 Gaussian elimination, 388 Gaussian integers, 173, 198, 452, 454 gcd
ideal, 5, 184 generated by, 5, 185 maximal, 188 prime, 188 principal, 5, 185 identity element, 125 identity map, xvi identity matrix, 378 image, xvi image of a random variable, 220 Impagliazzo, R., 274 inclusion map, xvi inclusion/exclusion principle, 209 independent, 213, 223 k-wise, 217, 224 mutually, 217, 224 index, 139 index calculus method, 419 index set, xv indicator variable, 221 infinite extension, 439 infinite order, 129 injective, xvi integral domain, 170 internal direct product, 147
Index
575
inverse multiplicative, 169 of a group element, 125 of a matrix, 385 inverse function, xvi invertible matrix, 385 irreducible element, 449 irreducible polynomial, 428 algorithm for generating, 522 algorithm for testing, 521 number of, 512 isomorphism algebra, 422 group, 145 module, 364 ring, 194 vector space, 369 Iwaniec, H., 338
of an integer, 61 Lenstra, Jr., H. W., 418, 545 Levin, L., 274 li, 116 linear combination, 360 linear map, 362 linear transformation, 499 linearly dependent, 366 linearly generated sequence, 484 minimal polynomial of, 485 of full rank, 490 linearly independent, 366 little-o, 49 Littlewood, J. E., 124 log, xiv logarithmic integral, 116 lowest terms, 12 Luby, M., 274, 303
Jacobi map, 345 Jacobi sum test, 558 Jacobi symbol, 344 algorithm for computing, 346 Jensen’s inequality, 239, 274
map, xvi Markov’s inequality, 240 Massey, J., 506 matrix, 376 matrix of a linear map, 382 Maurer, U., 323 maximal ideal, 188 memory cells, 52 Menezes, A., 101, 102 Mertens’ theorem, 112 Micali, S., 356 Miller, G. L., 322, 323 Miller–Rabin test, 305 Mills, W., 483, 506 min entropy, 265 minimal polynomial, 437 algorithm for computing, 466, 498, 524 of a linear transformation, 501 of a linearly generated sequence, 485 of an element under a linear transformation, 501 Möbius function (), 45 Möbius inversion formula, 46 mod, 4, 15, 21, 177, 434 modular square root algorithm for computing, 348 module, 357 modulus, 15 monic associate, 428 monic polynomial, 176 monomial, 182 Monte Carlo algorithm, 301 Morrison, K., 506 Morrison, M., 418 multi-variate polynomial, 183 multiple, 1, 169 multiple root, 181 multiplication map, 142, 166, 362 multiplicative function, 46 multiplicative group of units, 169 multiplicative identity, 26 multiplicative inverse in a ring, 169
Kalai, A., 303 Kaltofen, E., 546 Karatsuba, A. A., 71 Kayal, N., 547, 558 kernel, 141 kills, 159 Kim, S. H., 323 Knuth, D. E., 71, 72, 101 von Koch, H., 124 Kronecker substitution, 477 Krovetz, T., 274 Kung, H. T., 482 Lagrange interpolation formula, 434 Las Vegas algorithm, 301 Latin square, 130 law of large numbers, 241 law of quadratic reciprocity, 341 law of total probability, 214 lcm integer, 11 polynomial, 432 leading coefficient, 176 of a reversed Laurent series, 447 least common multiple in an integral domain, 450 integer, 10 polynomial, 431 leftover hash lemma, 266 Legendre symbol, 340 Lehmann, D., 323 Lehmer, D., 418 Leiserson, C. E., 338 len, 61, 464 length of a polynomial, 464
576 modulo integers, 20 modulo polynomials, 433 multiplicative order, 32, 152 multiplicative order modulo n, 32 multiplicity, 181 mutually independent, 217, 224 natural map, 142, 191 Newton’s identities, 448 Niven, I., 355 non-constant polynomial, 175 non-trivial ring, 167 norm, 166, 517 normal basis, 520 number field sieve, 418 Oesterlé, J., 124 one-sided error, 302 one-time pad, 228 one-to-one correspondence, xvi van Oorschot, P., 101, 102, 339 order in a module, 362 of a group element, 152 of an abelian group, 129 pairwise disjoint, xvi pairwise independent events, 217 hash functions, 251 random variables, 224 pairwise relatively prime integer, 11 polynomial, 432 partition, xvi Pascal’s identity, 560 Penk, M., 101 perfect power, 63 period, 97 periodic sequence, 97 PID, 452 pivot element, 388 pivot sequence, 387 Pohlig, S., 338 Pollard, J. M., 339, 418 polynomial associate, 428 irreducible, 428 monic, 176 primitive, 457 reducible, 428 polynomial evaluation map, 191, 424 polynomial time, 54 expected, 281 strict, 281 Pomerance, C., 72, 124, 323, 418, 419, 558 de la Vallée Poussin, C.-J., 123, 124 power map, 142 pre-image, xvi pre-period, 97 primality test
Index deterministic, 547 probabilistic, 304 prime ideal, 188 number, 2 prime number theorem, 115 irreducible polynomials over a finite field, 512 primitive polynomial, 457 principal ideal, 5, 185 principal ideal domain, 452 probabilistic algorithm, 276 probability distribution conditional, 212 discrete, 269 finite, 206 product distribution, 210 program, 52 projection, 490 public key cryptography, 339 public key cryptosystem, 98 purely periodic, 97 Q, xiv quadratic formula, 181 quadratic reciprocity, 341 quadratic residue, 35 quadratic residuosity algorithm for testing, 347 assumption, 354 quadratic sieve, 414 quantum computer, 419 quotient algebra, 422 quotient group, 139 quotient module (over R), 361 quotient ring, 186 quotient space, 369 R, xiv Rabin, M. O., 322 Rackoff, C., 418 RAM, 52 random access machine, 52 random self-reduction, 335 random variable, 220 conditional distribution of, 223 conditional expectation, 237 distribution of, 221 expected value, 233 image, 220 independent, 223 real valued, 220 variance, 235 random walk, 243 randomized algorithm, 276 rank, 394 rational function field, 427 rational function reconstruction, 472 rational reconstruction problem, 89 recursion tree, 330, 338 Redmond, D., 124 reduced row echelon form, 387
Index reducible polynomial, 428 Reed, I., 483 Reed–Solomon code, 95, 475 regular function, 222 relatively prime in an integral domain, 450 integers, 7 polynomials, 430 Renyi entropy, 265 rep, 64, 464 repeated-squaring algorithm, 64 representation, 335 representative of a coset, 137 of a residue class, 24 of an equivalence class, 15 residue class, 23, 186 residue class ring, 186 restriction, xvi reversed Laurent series, 446 Richert, H., 323 Riemann hypothesis, 117–119, 121, 123, 322, 324, 338, 355, 480, 545, 546 Riemann’s zeta function, 117 Riemann, B., 123 ring, 165 ring of polynomials, 175 ring-theoretic product, 168 Rivest, R. L., 98, 102, 338 Rogaway, P., 274, 419 root of a polynomial, 178, 424 Rosser, J., 123 row echelon form, 396 row null space, 392 row rank, 394 row space, 391 row vector, 377 RSA cryptosystem, 98 Rumely, R. S., 558 running time expected, 281 sample mean, 240 sample space, 206 Saxena, N., 547, 558 scalar, 357 scalar multiplication map, 357 Schirokauer, O., 419 Schoenfeld, L., 123 Schönhage, A., 71, 101 Scholtz, R., 506 Schoof, R., 101 secret sharing, 229 Semaev, I. A., 545 separating set, 543 sequence, xv Shallit, J., 124, 355, 545 Shamir, A., 70, 98, 102, 274 Shanks, D., 338 shift register sequence, 486 Shor, P., 419
Shoup, V., 338, 506, 545, 546 Shub, M., 102 sieve of Eratosthenes, 114 simple root, 181 smooth number, 398, 414 Solomon, G., 483 Solovay, R., 323, 355 Sophie Germain prime, 121 spans, 366 splitting field, 441 square root (modular) algorithm for computing, 348 square-free integer, 9 polynomial, 507 square-free decomposition, 526 square-free decomposition algorithm, 526 standard basis, 367 statistical distance, 259 Stein, C., 338 Stein, J., 101 Stinson, D. R., 274 Stirling’s approximation, 113 Strassen, V., 71, 323, 355 strict polynomial time, 281 subalgebra (over R) of E , 422 subfield, 174 subgroup, 131 generated by, 152 submodule (over R) of M , 359 subring, 172 subspace, 369 surjective, xvi theta function of Chebyshev, 106 total degree, 182 total probability law of, 214 trace, 517 transcendental element, 439 transpose, 379 trial division, 304 trivial ring, 167 twin primes conjecture, 122 two-sided error, 302 UFD, 449 ultimately periodic sequence, 97 union bound, 209 unique factorization in a Euclidean domain, 451 in a PID, 453 in DŒX, 457 in F ŒX, 428 in Z, 2 unique factorization domain, 449 unit, 169 universal hash functions, 251 Vandermonde matrix, 384 Vanstone, S., 101, 102
577
578 variance, 235 vector space, 369 Walfisz, A., 123 Wang, P., 101 Wang, Y., 338 Weber, D., 419 Wegman, N. M., 274 Weilert, A., 462 Welch, L., 506 well-behaved complexity function, 68 Wiedemann, D., 506 Wiener, M., 102, 339 Wright, E. M., 102, 123, 124 Yun, D. Y. Y., 545 Z, xiv Zassenhaus, H., 545 zero divisor, 170 zero matrix, 377 zero-sided error, 302 zeta function of Riemann, 117 Zuckerman, H., 355 Zuckermann, D., 274
Index
